An Urgent Need for Quantitative Intersectionality in Physical Activity and Health Research
Notice bibliographique
Résumé
The term intersectionality was originally introduced by Crenshaw 1 to highlight how Black women's experiences were marginalized via erasure within feminist movements.Using intersectional analysis, Crenshaw and others identified the modal "woman's experience" of oppression as a quintessentially White woman's experience in the absence of any consideration of race, as was common in early second-wave feminist organizing.Since Crenshaw's initial formulation, intersectionality has been extrapolated beyond gender and race to consider how many individual factors intersect with and impact each other in the lives of individuals and communities.Today, intersectionality is a theoretical and methodological framework for understanding the ways in which gender identity, gender expression, race/ethnicity, disability, class, age, and other social identities interweave in their impact on health and well-being.2,3 Acknowledging that interwoven forms of marginalization cannot be reduced to one particular factor, an intersectional approach considers how simultaneously belonging within multiple marginalized groups informs how one is impacted by marginalization.4 Intersectionality in Physical Activity ResearchEvidence from White settler colonies, such as Australia, Canada, South Africa, and the United States, as well as racially and ethnically diverse parts of the world consistently suggests that participation in regular physical activity, including sport, is the highest among well educated, wealthy, cis-heterosexual White, able-bodied men above all others.5,6 Barriers to physical activity participation in relation to race/ethnicity, gender identity and expression, ability, class, and other social position factors have predominantly been investigated using qualitative methods.7 This qualitative preponderance is partly attributable to a premise of intersectionality that social positions and relevant experiences cannot neatly fit into ordinal scales for metric statistical analysis.8 Nonetheless, one recent scoping review investigating the operationalization of intersectionality in physical activity research 9 suggested that intersectionality may also serve as a useful framework in quantitative research.In particular, elucidating complex processes of individual-and social-structural-level factors that drive inequalities in physical activity participation in populationbased, large-scale surveys could better inform more inclusive physical activity promotion policies and programs.In addition to the need for quantitative research applying intersectionality, the authors 9 of the review also suggested that intersectionality-based investigation in physical activity contexts has largely been limited to investigating the sex/gender + race/ethnicity dyad; thus, investigating varying axes of marginalization beyond the sex/gender + race/ethnicity dyad is important.Two recent reviews of qualitative studies examining LGBTQ+ adults' intersectionality-based experiences in sport settings 10,11 also highlighted that more intersectional research is required to better understand how more individuals with different memberships within marginalized groups can have quality opportunities and experience in physical activity.More recently, Joseph et al's 12 scoping review on racialized women in sport in Canada reiterated the importance of considering intersectional identities, suggesting that there is a lack of or no evidence investigating intersectionality-based experiences in sport beyond the sex/gender + race/ethnicity dyad (eg, no studies found focusing on the experience of sport among racialized trans women).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,299 | 0,291 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,020 | 0,025 |
| Études des sciences et des technologies | 0,008 | 0,026 |
| Communication savante | 0,028 | 0,057 |
| Science ouverte | 0,010 | 0,035 |
| Intégrité de la recherche | 0,007 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».