Additional file 1 of Gender-related variables for health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.781 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.999 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it