Additional file 1 of Gender-related variables for health research
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Additional file 1: Fig. S1. Flowchart of article inclusion and exclusion in the literature search. Fig. S2. Screeplot of the factor analysis reported in Table S8. Table S1. Item phrasing and descriptive statistics for the 44 potentially relevant gender-related items. Table S2. Response options for all 44 items included in the exploratory factor analyses. Table S3. Rank of gender characteristics based on occurrences (>2). Table S4. Search-terms for meta-analyses of existing scales measuring each gender variable. Table S5. Health-related items and response options. Table S6. Demographic items and response options. Table S7. Recoding of ten variables to allow for the largest possible sample in the EFA. Table S8. Exploratory Factor Analysis (Full factor model). Table S9. Communalities and unique variances for exploratory factor analysis presented in Table S8. Table S10. Exploratory Factor Analysis (Full factor model), Oblimin rotation. Table S11. Exploratory Factor Analysis (Full factor model), Varimax rotation. Table S12. Exploratory Factor Analysis (Full factor m odel), Equamax rotation. Table S13. Exploratory Factor Analysis (Full factor model), Quartimax rotation. Table S14. Factor loadings for CFA Models 1 and 2 in sample 1. Table S15. Factor loadings for CFA Samples 2 and 3 (Configural invariance). Table S16. Factor loadings for CFA Samples 2 and 3 (Metric invariance, 24 items). Table S17. Factor loadings for final CFA in samples 2 and 3 (Scalar invariance, 24 items). Table S18. Factor loadings for final CFA in samples 2 and 3 (Metric invariance, 25 items). Table S19. Factor loadings for final CFA samples 2 and 3 (Scalar invariance, 25 items). Table S20. Correlations between the factors in samples 1, 2 and 3. Table S21. Negative binomial regression predicting number of days with poor physical health (during past 30 days) (with gender identity as covariate). Table S22. Negative Binomial regression predicting number of days with poor mental health (during past 30 days) (with gender identity as covariate). Table S23. Negative binomial regression predicting number of days where poor mental or physical health prevented the respondent from doing usual activities (during past 30 days) (with gender identity as covariate). Table S24. Logistic regression predicting general health status (excellent, very good, good= 0, fair, poor= 1) (with gender identity as covariate). Table S25. Logistic regression predicting vaping (not vaping=0, vaping=1) (with gender identity as covariate). Table S26. Logistic regression predicting smoking (not smoking=0, smoking=1) (with gender identity as covariate). Table S27. Logistic regression predicting binge drinking (less than monthly=0, monthly, weekly, and daily=1) (with gender identity as covariate). Table S28. Logistic regression predicting overweight (BMI<25=0, BMI≥25 =1) (with gender identity as covariate). Table S29. Negative binomial regression predicting number of days with poor physical health (during past 30 days) (with sex as covariate). Table S30. Negative Binomial regression predicting number of days with poor mental health (during past 30 days) (with sex as covariate). Table S31. Negative binomial regression predicting number of days where poor mental or physical health prevented the respondent from doing usual activities (during past 30 days) (with sex as covariate). Table S32. Logistic regression predicting general health status (excellent, very good, good= 0, fair, poor= 1) (with sex as covariate). Table S33. Logistic regression predicting smoking (not smoking=0, smoking=1) (with sex as covariate). Table S34. Logistic regression predicting vaping (not vaping=0, vaping=1) (with sex as covariate). Table S35. Logistic regression predicting binge drinking (less than monthly=0, monthly, weekly, and daily=1) (with sex as covariate). Table S36. Logistic regression predicting Overweight (BMI<25=0, BMI≥25 =1) (with sex as covariate). Table S37. Negative binomial regression predicting number of days with poor physical health (during past 30 days) (combined samples). Table S38. Negative binomial regression predicting number of days with mental health (during past 30 days) (combined samples). Table S39. Negative binomial regression predicting number of days where poor mental or physical health prevented the respondent from doing usual activities (during past 30 days) (combined samples). Table S40. Logistic regression predicting general health status (excellent, very good, good= 0, fair, poor= 1) (combined samples). Table S41. Logistic regression predicting smoking (not smoking=0, smoking=1) (combined samples). Table S42. Logistic regression predicting vaping (not vaping=0, vaping=1) (combined samples). Table S43. Logistic regression predicting binge drinking (less than monthly=0, monthly, weekly, and daily=1) (combined samples). Table S44. Logistic regression predicting Overweight (BMI<25=0, BMI≥25 =1) (combined samples). Table S45. Final 25 survey items.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,781 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,999 | 0,001 |
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 découle