Damien Logearta
email: damien.logeart@aphp.fr

Thibaud Damyb

Maxime Doubletc

Muriel Salvatd

Christophe Tribouilloye

Fabrice Bauerf

Jean-Christophe Eicherg

Franc¸ Ois Picardh

Gérald Rouli

Jean-Noël Trochuj

Pascal De Grootek

Nicolas Bihryl

Emmanuelle Berthelotm

Guillaume Jondeaun

Marie-France Serondeo

Franc¸ Ois Roubillep

Richard Isnardq

ResearchFeasibility and accuracy of linking a heart failure registry to thenational claims database using indirect identifiers

Keywords: Heart failureOutcomeRegistriesClaims databases CKD, chronic kidney disease, COPD, chronic pulmonary obstructive disease, FRESH, FREnch Survey on Heart Failure, HF, heart failure, ICD-10, InternationalClassification of Diseases, Tenth Revision, LVEF, left ventricular ejection fraction, NIR, Numéro d'Inscription au Répertoire, SNDS, Système National System database), TAVI, transcatheter aortic valve implantation, TAVR, transcatheter aortic valve replacement

s•Follow-up of patients with HF in registries is a critical issue.

•Medicoadministrative databases contain long-term exhaustive data .•Linking accurately anonymized medicoadministrative database and HF registry is feasible .•The linking process increases the relevance of registries.

Background

The prevalence of heart failure (HF) in France is similar to that in other European countries, affecting around 2.3% of the adult population, with rates rising to 15.1% in those aged > 85 years [START_REF] De Peretti | Prévalenceset statut fonctionnel des cardiopathies ischémiques et de l'insuffisance car-diaque dans la population adulte en France: apports des enquêtes déclaratives« Handicap-Santé[END_REF]. To provide the best possible care to the most inclusive patient population, clinicians, caregivers and healthcare authorities need to be able to assess the efficiency, safety and outcomes of treatments, as well as gain insight into the quality and performance of the care provided in everyday practice.

For such assessments, data from real-life registries are valuable. Such registries can include clinical datasets with very high levels of details and granularity. A common problem with registries is that they depend on the availability of the information; however, the manual entry of information adds to the ordinary workload of healthcare personnel, and frequently leads to gaps in the records and major losses to follow-up [START_REF] Bufalino | TheAmerican Heart Association's recommendations for expanding the applicationsof existing and future clinical registries: a policy statement from the AmericanHeart Association[END_REF]. Independent verification of collected data increases the data reliability, but with a dramatic impact on costs, and thus is rarely done.

The explosive growth in electronic health databases in recent years has made it possible to enrich the completeness of registries and enhance the value of analyses by linking multiple data to claims databases [START_REF] Harron | Challengesin administrative data linkage for research[END_REF]. The French single-payer National Healthcare System database (SNDS; Système National des Données de Santé) includes reimbursement information on close to 99% of the French population (or > 68 million people), making it one of the world's largest continuous claims databases [START_REF] Bezin | The national health-care system claims databases in France, SNIIRAM and EGB: Powerful tools forpharmacoepidemiology[END_REF]. The SNDS also includes data onmortality, hospitalizations and long-term diseases. The French version of the International Classification of Diseases, Tenth Revision(ICD-10) [START_REF] Bousquet | Evaluation of theCCAM Hierarchy and Semi Structured Code for Retrieving Relevant Proceduresin a Hospital Case Mix Database[END_REF] is used to identify reasons for hospitalizations and long-term diseases; this includes HF (ICD-10 codes I50.x and R570).However, although the accuracy of ICD-10 is good, it does not capture all patients with HF. Only a proportion of patients with HF are hospitalized during the analysed time, and some are not coded as having HF-related long-term disease because of previous coding for co-morbidities, such as ischaemic heart disease or diabetes. The sensitivity of coding for HF has been estimated at between 29% and86% in several studies [START_REF] Bosco-Levy | Diag-nostic accuracy of the International Classification of Diseases, Tenth Revision,codes of heart failure in an administrative database[END_REF][START_REF] Ingelsson | The validity of a diagnosis of heartfailure in a hospital discharge register[END_REF][START_REF] Khand | Do discharge codes underestimatehospitalisation due to heart failure? Validation study of hospital dischargecoding for heart failure[END_REF][START_REF] Kumler | Accu-racy of a heart failure diagnosis in administrative registers[END_REF][START_REF] Mccormick | Validity of heart failurediagnoses in administrative databases: a systematic review and meta-analysis[END_REF]. Furthermore, only few clinical data are captured when using ICD-10 codes. As a relevant example for HF, information on left ventricular ejection fraction (LVEF) was not available in the database until the 2019 update of ICD-10 codes. On the other hand, records of events, hospitalization, discharge and mortality tend to be much more complete and accurate than in registries.

We have previously demonstrated the feasibility of linking a registry and SNDS data for patients undergoing transcatheter aortic valve implantation (TAVI) to minimise follow-up losses and close data close data gaps [START_REF] Didier | Successful linkage of French large-scale national registry populations to nationalreimbursement data: improved data completeness and minimized loss tofollow-up[END_REF]. However, patients undergoing TAVI represent a comparatively homogeneous (older) population undergoing a specific and easily identified treatment, whereas HF is a syndrome that manifests in a multitude of ways, and its treatment, management and outcomes vary widely between individuals.

Hence, a linkage attempt will face greater challenges than for populations undergoing specific procedures. The objective of the current study was to assess the accuracy of linking a research HF registry (FRESH; FREnch Survey on Heart Failure) to the SNDS, as well as to evaluate differences and discrepancies between the two data sources.

Methods

Data sources

The FRESH registry was set up in 2014 with the aim of providing detailed information on several aspects of HF and its treatment nationally. The registry gathers data on the characteristics of the population with HF, including socioeconomic status, aetiology of the disease, medical history and co-morbidities, information about the care provided to manage HF at baseline, and follow-up data on rehospitalization and death. A total of 2719 patients were included at 32 centres across France between 18 February 2014 and 31December 2017. The registry is registered on ClinicalTrials.gov(identifier: NCT01956539), and it is supported by the French Cardiology Society

The FRESH registry enrolled patients aged > 18 years with a diagnosis of HF during both outpatient consultation and unplanned hospitalization for acute HF. Recruitment was 1 day per week. To enable analysis of the course of disease and changes in treatment patterns over time in individual patients, including the impact of new guideline recommendations, patients were followed up at 12 and 24 months. Patients included at the time of an episode of acute HF were also followed up 3 months after the event. There was no independent verification of collected data. The SNDS combines health insurance data (SNIIRAM database), hospital data (PMSI database), medical causes of death (Cépi DC database),disability-related data and data from complementary health insurance organizations, but no clinical, biological or echocardiographic data. The organization of the SNDS has been described previously [START_REF] Didier | Successful linkage of French large-scale national registry populations to nationalreimbursement data: improved data completeness and minimized loss tofollow-up[END_REF][START_REF] Boudemaghe | Data Resource Profile: The French National UniformHospital Discharge Data Set Database (PMSI)[END_REF][START_REF] Tuppin | Value of a national administrative database to guidepublic decisions: From the systeme national d'information interregimes del'Assurance Maladie (SNIIRAM) to the systeme national des donnees de sante(SNDS) in France[END_REF]. The database contains more than 1.2 billion yearly claims records and 11 million hospitalizations [START_REF]Qu'est-ce que le SNDS ?[END_REF]. Entries are anonymized as a unique number "Numéro d'Inscription au Répertoire" (NIR) for each patient, and records contain no information that allows direct identification of individuals [START_REF] Boudemaghe | Data Resource Profile: The French National UniformHospital Discharge Data Set Database (PMSI)[END_REF]. SNDS data were analysed for the period 2012 to 2018, starting 2 years before, and covering up to 5 years after enrolment in the FRESH registry. Data collected up to 31 December 2017 in the FRESH registry were included in the FRESH-SNDS linkage.

Linkage process

The development of the linking algorithm and its core features has been described previously [START_REF] Didier | Successful linkage of French large-scale national registry populations to nationalreimbursement data: improved data completeness and minimized loss tofollow-up[END_REF]. Because current regulations prohibit identification of individuals in the SNDS to match them directly with registries, the linkage process employs a probabilistic approach, based on matching SNDS data as closely as possible to the profiles in the FRESH registry. An initial filtering was performed, utilising a selection of primary anonymous identifiers, including sex, year of birth, department of residence, date of consultation or hospitalization, date of discharge for hospitalized patients and hospital identification number. For patients with missing data, the match was refined by the inclusion of secondary identifiers, which were not utilised in the primary linking process, i.e. month of birth and area of residence.

The final quality of linking was assessed by the consistency rates for deaths between the two data sources. The objective was to generate a linked database with the highest consistency rate possible, which was estimated at ≥ 90% for deaths between the two databases. A death recorded in the SNDS was considered identic alto one in the FRESH registry if the registry date fell within 60 days of that recorded in the SNDS. Because of delay between the date of a death and its inclusion in the SNDS, deaths were considered in the SNDS up to 31 December 2018. In the case of insufficient consistency rate for deaths, we planned additional scoring methods with several identifiers, including some of previous administrative variables, but also vital status and highly identifying clinical variables, such as diabetes. The most reliable model had to result in a consistency rate of > 90% for deaths, and with the highest rate of matched patients.

Comparison of the FRESH and SNDS-FRESH linked databases

In the FRESH registry, clinical characteristics were entered on case report forms by local investigators at the time of a consultation or hospitalization. In the SNDS, only some of these variables could be approximated using a case-identifying algorithm, including ICD-10 code-related hospitalizations or long-term diseases, administrative codes related to the reimbursement of procedures (Classification Commune des Actes Medicaux (CCAM) or Anatomical Therapeutic Chemical (ATC) codes.

The two data sources were compared on the following variables, which were present in both databases: age; sex; diabetes; ischaemic heart disease; chronic pulmonary obstructive disease(COPD); end-stage chronic kidney disease (CKD); atrial fibrillation; stroke; and HF hospitalization within the previous 12 months. Algorithms used in the SNDS for each variable are detailed in Appendix B. These characteristics were compared for exhaustiveness of the data (proportion of missing data) and consistency rates, i.e. the pro-portion of patients with matching information between the two data sources.

In addition, deaths and hospitalizations occurring in the two data sources during the 1-year follow-up after inclusion were com-pared. Only hospitalizations lasting at least 2 nights were taken into account. Hospitalization in follow-up care and rehabilitation were identified and excluded. In the SNDS, HF hospitalization was identified by ICD-10 codes I50 and R570 as the main or associated diagnosis.

Ethical considerations

All patients in the FRESH cohort provided written informed con-sent. Ethics approval to link the FRESH registry and the SNDS was obtained from the French Expert Committee on Research, Studies and Assessments in Health (CEREES) on 25 April 2019 (TPS347113bis), and from the Commission Nationale de l'Informatique et des Libertés (CNIL) on 19 November 2019 (DR-2019-34). All data were analysed anonymously. Each individual record in the SNDS was randomly assigned a numerical identity (different from the NIR), which included no information about the identity of the patient or centre. This number was used in the analysis, with no reverse identification possible. A different individual identity was assigned to each patient in the FRESH cohort. All transmissions of data were encrypted and stored on secure servers. At no point did the analysts have access to original names or NIRs. Because all patient data were from a registry that had already obtained informed consent and ethical clearance, no additional ethical approval was necessary.

Results

Linkage process

The result of the linkage process is shown schematically in Fig. 1. The FRESH cohort included 2719 patients with available data:1049 (38.6%) subjects were included during a hospitalization for acute HF; and 1670 (61.4%) were included during an outpatient consultation. The initial linkage process identified 2412 (88.7%)individuals with matching identifiers in the SNDS and the FRESH registry. After this first linking process, 25% of deaths were irreconcilable between the two databases. The rate of inconsistencies was similar for patients who were included during HF-related hospitalization and outpatients. The linking process was refined further using scoring methods. The most reliable model required that at least three of the following four identifiersbirth month, consultation/hospitalization date, diabetes, vital statuswere identical in the two databases. This resulted in a cohort of 1885 (69.3%) linked patients, with a 94.3% consistency rate for deaths. COPD: chronic obstructive pulmonary disease; end-stage CKD: chronic kidney disease with glomerular filtration rate < 15 mL/min; FRESH: FREnch Survey on Heart Failure; HF: heart failure; SNDS: Système National des Données de Santé (French National Healthcare System database).a Hospitalization for HF was defined by using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code I50 or R570 as the main diagnosis.

Comparison of clinical characteristics between the two databases

A comparison of demographic and clinical characteristics fort he linked population, based on data in the two source databases, is shown in Table 1. In the FRESH registry, demographic variables were available for all patients, but some data were missing for co-morbidities (COPD, CKD, atrial fibrillation, stroke; 2.3-8.4%), and data on history of HF hospitalization before inclusion were unavailable in 3.7% of patients. Recorded rates of diabetes, stroke and atrial fibrillation were higher in the FRESH registry than in the SNDS, whereas rates of COPD, end-stage CKD and previous HF hospitalization were higher in the SNDS.

There was 100% consistency between the FRESH registry and the SNDS for age and sex, which were used for the linkage process. For co-morbidities, the consistency varied between 76.9% and97%. In an analysis of subpopulations, the concordance rate for diabetes and atrial fibrillation was higher among patients include during hospitalization (96.3 and 82.5%, respectively) than among those included during outpatient consultation or day hospitalization (87.6% and 73.0%, respectively).

Outcomes and comparison between the two databases

Rates of deaths at 1 year were 12.0% in the SNDS and 11.5% in the FRESH registry. Fig. 2 compares survival curves from the FRESH registry and the SNDS. Fig. 3A and Fig. 3B show survival curves from the FRESH registry and the SNDS, respectively, according to the following subgroups, based on type of HF at inclusion: patients hospitalized for acute HF; outpatients (chronic HF); patients with a reduced LVEF; and patients with a preserved LVEF.

Because of different definitions and causes of hospitalization in the two databases, no direct comparison between the FRESH registry and the SNDS was carried out for this variable. In the FRESH registry, data on all-cause hospitalization were missing in 17.5% of patients, and the cause of hospitalization was unknown in 29.3%.For patients with collected data, the rate of patients with at least one all-cause hospitalization was 46.2% at 1 year. This rate fell to 29.6% if only unplanned hospitalizations were considered. In the SNDS, the rate of patients with at least one all-cause hospitalization at 1 year was 69.7%; the rate was 55.9% if only hospitalizations with at least2 nights were considered. The rate of HF-related hospitalizations at 1 year in the SNDS (ICD-10 codes I50 and R570 as main diagnosis and hospitalizations ≥ 2 nights) was 20%; the rate was 30.2%if HF hospitalizations included ICD-10 codes I50 and R570 as the main or associated diagnosis. Fig. 4 shows all-cause and HF-related hospitalizations in the SNDS. Fig. 5 shows the occurrence of all-cause hospitalizations, according to the type of HF at inclusion.

Discussion

The results presented here show that it is possible to link and match records in the national healthcare database with patient data from the case report forms of a pre-existing HF registry. Such linkage markedly improves the quality of follow-up data.

The development of a probabilistic algorithm for HF is considerably more challenging than the earlier reported linkage of transcatheter aortic valve replacement (TAVR) registries-FRANCE-2 and FRANCE-TAVI. HF is a frequent disease, with various patient profiles and treatments; this creates at least two serious problems for linking processes. The first is the inclusion of outpatients who have no ICD-10 code-related hospitalization to help their identification in the SNDS during the study period. The second is the lack of a medical procedure common to all patients with HF, which contrasts with, for example, valve implantation. Considering these hurdles, the 69.3% completeness achieved with the multistep algorithm appears quite respectable, even if lower than the 89.9% complete-ness achieved for the TAVR registries and the SNDS [START_REF] Didier | Successful linkage of French large-scale national registry populations to nationalreimbursement data: improved data completeness and minimized loss tofollow-up[END_REF]. Linkage generates an enriched population, which can be characterised in greater depth than the isolated underlying datasets. Registries are key to evaluating quality and effectiveness of care under everyday conditions; they also typically include variables that are not available because they do not correspond to ICD-10codes in healthcare databases, e.g. renal function, body mass index and functional class. However, the quality of a registry is only as good as that of the data, which are entered by investigators on a voluntary basis. For outcomes, a registry will rely on calling sub-jects or family or referring doctors. Such follow-up requires great and time-consuming efforts, and losses to follow-up can be significant, especially when the duration of follow-up is long, as in HF. The regulatory requirements and needs underlying a national administrative claims database mean that it will have a high cover-age of clinical events, which incur costs, as well as consumption of care and medication. Events such as death or hospitalizations are covered, with few gaps. This is reflected in the high rate of hospitalizations in the linked population compared with, for example,(completed) case reports. It is interesting to note that different co-morbidities were covered to different degrees of completeness in the two source databases. In the healthcare database, co-morbidities are deter-mined by administrative codes and the use of case-identifying algorithms. In the FRESH registry, as in most registries, reliability of co-morbidity data depends on how patients are asked and the patient's knowledge of their conditions, which can be critical for some comorbidities. We note that there was greater consistency for diabetes and atrial fibrillation in acute HF than in the outpatient setting. This may be partially explained by repeated and/or extended examination during hospitalization. The fact that atrial fibrillation and stroke were more frequently reported in the FRESH registry can be explained by the limited medical history captured in the SNDS. Atrial fibrillation was recorded only for the 2 latest years before inclusion, and on the basis of interventions or administered antiarrhythmic medications, which may not be considered necessary in many milder or paroxysmal cases. For stroke, the identification in SNDS was based on stroke-related hospitalization, also only during the 2 latest years before inclusion, which will have underestimated the rate of previous strokes. Fig. 5. All-cause hospitalizations, according to the following subgroups: patients hospitalized for acute heart failure; outpatients; patients with a left ventricular ejection fraction (LVEF) ≤ 45%; and patients with an LVEF > 45%.

On the other hand, COPD and end-stage CKD were covered more completely in the SNDS. For COPD, the diagnosis was based on concomitant therapies in the SNDS. Although such medications are also prescribed for asthma, the age of the population makes this an unlikely confounder. The reasons for different degrees of completeness of co-morbidity entries would need further analysis, but it appears that both registries and medical claims databases have advantages and limitations, with no clear winner in this respect.

One of the main advantages of healthcare databases, such as the SNDS, over most registries is the completeness of events and their dates during follow-up. Such completeness is often difficult to obtain in academic registries because of the required capacity In the FRESH registry, the rate of missing data on hospitalization was 17.5%, and the rate was even greater when the cause and/or the date of hospitalization were considered.

Consistency between the two databases can be altered by differences in dates. In the current analysis, we limited follow-up to1 year; in studies with a longer follow-up, an even higher degree of completeness of the SNDS data compared with registries can be expected. For patients with data in the FRESH registry, the rate of unplanned hospitalizations was lower than in the SNDS, although it can be difficult to differentiate planned and unplanned hospitalization in the SNDS, which is a major limitation of medico administrative databases. To reduce the number of erroneously included planned hospitalizations in the analysis of unplanned hospitalizations in the SNDS, we removed day hospitalization and hospitalization for < 2 nights, as these were unlikely to be hospitalizations for decompensation. Only hospitalizations ≥ 2 nights were considered in the SNDS, to avoid elective planned hospitalizations. Definitions of events in the SNDS are continually being revised with the arrival of new information and updates to guidelines as medical knowledge advances, which, although positive for the quality of the database, may weaken correlations with registry data.

Another issue with medico administrative databases is the presence of one main diagnosis and one or several associated and related diagnoses. For the same patient, HF code I50x can be used either as the main diagnosis or as an associated diagnosis, depending on the person in charge of administrative coding. For example, decompensated HF as a result of pneumopathy can lead to an "I50"coding as the main diagnosis or only as an associated diagnosis. Consequently, it is useful to show the two rates of HF-related hospitalizations according to either the single use of a main diagnosis or the use of main and associated diagnoses. Although it is imperfect, the reliability of HF coding in the SNDS was validated in a recent study [START_REF] Bosco-Levy | Diag-nostic accuracy of the International Classification of Diseases, Tenth Revision,codes of heart failure in an administrative database[END_REF]. A weakness of the current study is that events collected in the FRESH registry were not adjudicated by a blinded committee.

Conclusions

In summary, this study showed that a significant proportion of patients from an HF registry could be matched anonymously to the national healthcare system database (SNDS) with high accuracy. This generated a database that will permit analyses that are not possible in the separate registriesin our case, outcomes in sub-populations with preserved versus reduced LVEF or acute versus chronic HF. The success with a patient database typical of a heterogeneous syndrome, such as HF, indicates that the methodology maybe applied to the linking of other registries in less distinctly defined populations; this could improve quality of data (fewer losses to follow-up, better accuracy) and improve the analytical power. The potential of the FRESH-SNDS linked database will be explored in further studies (central illustration).

Fig. 1 .

 1 Fig. 1. Schematic representation of the linkage process between the FREnch Survey on Heart Failure (FRESH) registry and the French single-payer National Healthcare System database (Système National des Données de Santé [SNDS]).

Fig. 2 .

 2 Fig. 2. Survival curves from the FREnch Survey on Heart Failure (FRESH) registry and the French National Healthcare System database (Système National des Données de Santé [SNDS]) for all-cause mortality.

Fig. 3 .

 3 Fig. 3. A-B. Survival curves from the FREnch Survey on Heart Failure (FRESH) registry (A) and the French National Healthcare System database (Système National des Données de Santé [SNDS]) (B) for all-cause mortality, according to the following subgroups: patients hospitalized for acute heart failure; outpatients; patients with a left ventricular ejection fraction (LVEF) ≤ 45%; and patients with an LVEF > 45%.

Fig. 4 .

 4 Fig. 4. All-cause hospitalizations and heart failure (HF)-related hospitalizations from the French National Healthcare System database (Système National des Données de Santé [SNDS]).

  Central illustration. Linking between Heart Failure registry and national healthcare database resulting in enriched data especially due to the exhaustive follow-up. CRF: case report form; LVEF: left ventricular ejection fraction; PMSI: Programme de Médicalisation des Systèmes d'Information (French hospital discharge database); SNDS: Système National des Données de Santé (French National Healthcare System database); SNIIR-AM: Système National d'Informations Inter-Régimes de l'Assurance Maladie (French outpatient claims database).

Sources of funding

Funded by grants from Amgen, Astra Zeneca, Bayer, Boehringer-Ingelheim and Novartis..

Disclosure of interest

The authors declare that they have no competing interest.


Aïna Chalabaev
email: aina.chalabaev@ujf-grenoble

Philippe Sarrazin
email: philippe.sarrazin@ujf-grenoble.fr.aïna

Paul Fontayne

STEREOTYPE ENDORSEMENT AND GIRLS' SOCCER PERFORMANCE Stereotype endorsement and perceived ability as mediators of the girls' gender orientation-soccer performance relationship

Keywords: Gender stereotypes, Stereotype endorsement, Sports performance, Perceived ability, Gender role

Objective: This study investigated girls' endorsement of the stereotype that girls are not good soccer performers through three questions: (1) Did stereotype endorsement predict soccer performance ? (2) Was this relationship mediated by perceived ability? and (3) Was stereotype endorsement related to gender role orientation? Method: One-hundred-and-two junior high school girls from the 6th to the 9th grade (Mage = 13.5 years, SD = 1.23) reported their beliefs about girls' and boys' performance in soccer, perceived ability and gender role orientation. Next, their soccer performance was observed during matches in compulsory physical education classes. A path-analytic model tested the relationships among the variables using a product-moment correlation matrix and a maximum likelihood estimation procedure.

Results: Stereotype endorsement (i.e., the belief that girls' performance in soccer is poor) negatively predicted performance, this relationship being mediated by perceived ability.

Moreover, masculinity positively predicted perceived ability, and this relationship was partially mediated by stereotype endorsement.

Conclusion: This study reinforces the idea that girls' sports performance may be related to gender stereotypes. Interpretations of the results in light of Eccles et al.'s expectancy-value model and stereotype threat theory are discussed, along with implications for practice.

Introduction

Although female sports participation has recently increased (e.g., [START_REF] Riemer | Gender typing of sports: An investigation of Metheny's classification[END_REF], boys perform still better in physical education (PE) classes (French Ministry of Education, 2000). This may be due to the influence of the stereotype that many sports are masculine, which has been shown to affect perceived ability in sports (e.g., [START_REF] Fredricks | Family socialization, gender, and sport motivation and involvement[END_REF]. However, although one may have knowledge of a cultural stereotype, his or her personal beliefs may or may not be congruent with the stereotype [START_REF] Devine | Stereotypes and prejudice: Their automatic and controlled components[END_REF]. In other words, it is important to investigate stereotype endorsement in order to have a deeper understanding of the influence of gender stereotypes on sports performance, and this was the goal of the study.

A few research reported an effect of stereotype endorsement on perceived ability (e.g., [START_REF] Bonnot | Stereotype internalization and women's math performance: The role of interference in working memory[END_REF][START_REF] Schmader | The costs of accepting gender differences: The role of stereotype endorsement in women's experience in the math domain[END_REF]), but only one study to our knowledge has investigated its effects on performance [START_REF] Belcher | The influence of gender-related beliefs and conceptions of ability on women learning the hockey wrist shot[END_REF]: girls considering hockey as masculine performed lower than girls perceiving it as neutral. However, these latter girls watched a same-sex model proficiently executing the task, and this may have explained the results (e.g., [START_REF] Gould | The effects of model similarity and model talk on selfefficacy and muscular endurance[END_REF]. A first goal of this study was to examine stereotype endorsement as a predictor of performance and the mediation of this relationship by perceived ability. Second, as research has shown that people who endorse the attributes considered as appropriate to their sex (e.g., women endorsing feminine attributes) are likely to assess the appropriateness of sports on a gender basis (e.g., [START_REF] Koivula | Ratings of gender appropriateness of sports participation: Effects of gender-based schematic processing[END_REF], a second goal was to study masculinity (M) and femininity (F) as antecedents of stereotype endorsement. The tested model is presented in Figure 1.

Method

Participants and procedure

One-hundred-and-two girls (Mage = 13.5 years, SD = 1.2) from the 6 th to the 9 th grade of three French junior high schools participated in this correlational study. Data were collected in PE classes during compulsory soccer lessons, a sport perceived as masculine in Europe [START_REF] Koivula | Ratings of gender appropriateness of sports participation: Effects of gender-based schematic processing[END_REF]. Students filled out a questionnaire, and after a 10-minute warming up, the teacher constituted three-player teams of same level students (i.e., high-level players together and low-level players together). Next, students played four-minute matches based on the "up-and-down" rule, which favours an equal rapport of strength: the fields were numbered, and at the end of each game, the winning team "moved up" from for example field #3 to #2, whereas the losing team "moved down". The experimenter, who was an experienced soccer player and blind to participants' responses to the questionnaire, observed the players of one team per game, each team being observed once.

Measures

Perceived ability in soccer was assessed by three items on a 7-point scale ranging from (1) very poor to (7) very good (e.g., "I think that my level of performance in soccer is:"), used by [START_REF] Duda | Dimensions of achievement motivation in schoolwork and sport[END_REF] and adapted to soccer (α = .74). [START_REF] Bonnot | Stereotype internalization and women's math performance: The role of interference in working memory[END_REF] measure, including two items relative to the level of soccer performance students personally assigned to girls and boys (e.g., "personally, I think that girls' performance in soccer is") on a scale ranging from (1) very poor to (7) very good. The order of these two items was counterbalanced. The more participants believed that girls' performance was poor controlling for boys' performance, the more they endorsed the stereotype.

Stereotype endorsement was assessed with

Masculinity and femininity. Participants filled out the validated French short version

for teenagers of the Bem Sex Role Inventory [START_REF] Fontayne | The Bem Sex-Role Inventory: Validation of a short-version for French teenagers[END_REF] on a 7point Likert scale. Athleticism, leadership, and self-confidence compose the M orientation (α = .77). Tenderness and sensitivity to others compose the F orientation (α = .69).

Soccer performance. A measure elaborated by [START_REF] Gréhaigne | Performance assessment in team sports[END_REF] was used. Unlike standardized tests assessing only technical skills, this measure also assesses tactical skills, indexing thus more accurately one's overall soccer ability. Four elements compose it: (1) offensive balls (OB): the successful passes to a partner; (2) successful shots on goal (SS); (3) conquered balls (CB): the balls intercepted or stolen from an opponent, and (4) lost balls (LB): the balls lost to the other team. The formula used to calculate performance was: (OB+SS+CB) / (10+LB). The more the index is positive, the higher the performance. [START_REF] Gréhaigne | Performance assessment in team sports[END_REF] showed a good interobserver reliability of this index between four raters (.90) using the intraclass correlation technique, and a good performance stability (.87) over a one-week period. They also reported a good concurrent validity, the index being related with a different soccer performance measure (r = .74). In the current study some matches were videotaped on a random basis, and a second observer rated four teams (i.e., 12 students), representing approximately 10% of the sample. The intraclass correlation was very good (.92), showing a good interobserver reliability.

Results

A path-analytic model tested the presumed relationships among the variables with the EQS 6.1 program, using a product-moment correlation matrix and a maximum likelihood estimation procedure 1 . The adequacy of the proposed model 2 was evaluated using four indices: (a) the chi-square distribution under the null hypothesis, (b) the normal fit index (NFI; [START_REF] Bentler | Significance tests and goodness-of-fit in the analysis of covariance structures[END_REF], (c) the comparative fit index (CFI; [START_REF] Bentler | Comparative fit indexes in structural models[END_REF], and (d) the root mean square error of approximation (RMSEA). The adequacy of the model was good: χ 2 (2) = 1.59, p = .45, NFI = .98, CFI = 1, RMSEA = .00. Figure 1 shows the standardised path coefficients. First, belief that girls' performance in soccer is poor 3 negatively predicted perceived ability (β = -.26, p = .002), which significantly predicted performance (β = .30, p = .002). Belief about girls' performance did not predict performance (β = -.13, p = .18), after controlling for the effect of perceived ability, whereas the direct correlation between these variables was significant (r = -.21, p < .05). This suggests that perceived ability mediated this relationship. Next, M marginally predicted belief about girls' performance (β = -.18, p = .07), and significantly predicted perceived ability (β =.44, p < .01) and belief that boys' performance in soccer is good (β = -.21, p = .04). This suggests that stereotype endorsement partially mediated the relationship between gender role and perceived ability. Finally, the analyses did not reveal any significant relationship involving F.

Discussion

First, results showed that the more girls believed in the negative ingroup stereotype associated with soccer (i.e., that girls' performance is poor), the lower they performed, this relationship being mediated by a low perceived ability. These findings provide an extension to the literature by showing that stereotype endorsement may predict not only perceived ability, but also actual performance. They also demonstrate the role of perceived ability in the mediation of this relationship, reinforcing Eccles et al.'s expectancy-value model (e.g., [START_REF] Fredricks | Family socialization, gender, and sport motivation and involvement[END_REF]. These results may be understood in terms of a stereotype internalisation hypothesis: the exposure to cultural stereotypes during the socialisation process may lead some people to believe that the stereotypes are true for themselves, resulting in poor performance in the stereotyped domain (e.g., [START_REF] Bonnot | Stereotype internalization and women's math performance: The role of interference in working memory[END_REF].

Stereotypes may also affect performance in other ways. For example, a more situational interpretation would be to consider the results in terms of stereotype threat (e.g., [START_REF] Steele | Stereotype threat and the intellectual test performance of African Americans[END_REF]: when a negative ingroup stereotype is made salient in a testing situation, people may fear confirming the stereotype, this extra pressure impeding their performance. A study recently showed that females may be susceptible to this effect in soccer [START_REF] Chalabaev | Do achievement goals mediate stereotype threat? An investigation on females' soccer performance[END_REF]. However, stereotype threat is not supposed to affect perceived ability (e.g., Steele & Aronson, 1995, Study 1), but instead to create an "interpretative framework" that when difficulty is experienced during the task, it reduces selfefficacy, which may evolve during the task and is thus a more situation-specific selfassessment than perceived ability [START_REF] Ryan | Psychological processes underlying stereotype threat and standardized math test performance[END_REF]. In this study we did not examine girls' self-efficacy during the task, but rather their perception about their general soccer ability prior to the evaluation. In sum, if Eccles et al.'s expectancy-value model seems to be more appropriate to interpret our results, it is important to note that stereotypes may affect performance in many ways that may add up to each other. It would be interesting to distinguish in future studies the gender stereotypes effects due to stereotype internalisation from the effects due to the presence of stereotypes in the social context.

Next, results showed that M negatively predicted stereotype endorsement, but not F.

According to the differentiated additive androgyny model (e.g., [START_REF] Marsh | Differentiated additive androgyny model: Relations between masculinity, femininity and multiple dimensions of self-concept[END_REF], the contribution of M is more important in masculine domains whereas the contribution of F is more important in feminine domains. This idea has been corroborated in the sports areas (e.g., [START_REF] Guillet | Understanding female sport attrition in a stereotypical male sport within the framework of Eccles' s expectancyvalue model[END_REF], and could thus explain why F did not affect stereotype endorsement and perceived ability in soccer.

Although this research reveals both some consequences and antecedents of stereotype endorsement, the results need to be considered with care. As with all path analytic studies, it is always possible that a relevant variable was omitted. Also, we cannot talk about causal relations between the variables: indeed, symmetrical relations may exist. For example, based on the identity bifurcation theory [START_REF] Pronin | Identity bifurcation in response to stereotype threat: Women and mathematics[END_REF]), identification to soccer (i.e., non-endorsement of the stereotype) may lead to the depreciation of feminine characteristics in favour of masculine ones.

To conclude, this study reinforces the idea that girls' poor performance in PE classes is in part due to the influence of gender stereotypes and highlight the role of personal beliefs in this relationship. This implies that it could be useful to design intervention programs based Stereotype endorsement and soccer performance 7 on changing girls' perceptions about gender appropriateness of sports in order to enhance 1 their performance in masculine sports. Footnotes 1. The correlation matrix is available from the first author of the article.

2. Different models were also tested but not reported in the paper. For example, the direct relations between M, F and performance were formulated, but the paths between these variables were not significant (ts <1.09). Thus, this model is not discussed any further.

Moreover, the interaction between M and F was not significantly related to the other variables and was thus not included in the model tested in this study.

3. In order to clarify the interpretations, the response to the belief about girls' performance item was reversed.

Stereotype endorsement and soccer performance 11

Figure 1 .

 1 Figure 1. Results of the path analysis model testing the relationships between the variables of the study.