Looking twice at the gender equity index for public health impact
Bibliographic record
Abstract
BACKGROUND: It has been shown that gender equity has a positive impact on the everyday activities of people (decision making, income allocation, application and observance of norms/rules) which affect their health. Gender equity is also a crucial determinant of health inequalities at national level; thus, monitoring is important for surveillance of women's and men's health as well as for future health policy initiatives. The Gender Equity Index (GEI) was designed to show inequity solely towards women. Given that the value under scrutiny is equity, in this paper a modified version of the GEI is proposed, the MGEI, which highlights the inequities affecting both sexes. METHODS: Rather than calculating gender gaps by means of a quotient of proportions, gaps in the MGEI are expressed in absolute terms (differences in proportions). The Spearman's rank coefficient, calculated from country rankings obtained according to both indexes, was used to evaluate the level of concordance between both classifications. To compare the degree of sensitivity and obtain the inequity by the two methods, the variation coefficient of the GEI and MGEI values was calculated. RESULTS: Country rankings according to GEI and MGEI values showed a high correlation (rank coef. = 0.95). The MGEI presented greater dispersion (43.8%) than the GEI (19.27%). Inequity towards men was identified in the education gap (rank coef. = 0.36) when using the MGEI. According to this method, many countries shared the same absolute value for education but with opposite signs, for example Azerbaijan (-0.022) and Belgium (0.022), reflecting inequity towards women and men, respectively. This also occurred in the empowerment gap with the technical and professional job component (Brunei:-0.120 vs. Australia, Canada Iceland and the U.S.A.: 0.120). CONCLUSION: The MGEI identifies and highlights the different areas of inequities between gender groups. It thus overcomes the shortcomings of the GEI related to the aim for which this latter was created, namely measuring gender equity, and is therefore of great use to policy makers who wish to understand and monitor the results of specific equity policies and to determine the length of time for which these policies should be maintained in order to correct long-standing structural discrimination against women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".