Suggestions for a gender-sensitive and intersectional practice of health monitoring and reporting
Bibliographic record
Abstract
Abstract Background Health reports summarize the evidence basis on disease burden and its causes and are intended to inform decisions of policy makers. By focusing on health needs of social groupings according to sex/gender or race/ethnicity, PHMR crucially contributes to achieving health equity. In order to realise its aims, PHMR relies on the availability of high-quality data, appropriate analysis methods and intuitive presentation of results. Methods The joint project AdvanceGender used mixed methods to translate principles of intersectionality into new methods for recruitment, data analysis and health reporting. A review of descriptions of representativeness in epidemiological studies was conducted to investigate how an intersectional perspective can inform recruitment. To evaluate intersectional and gender-sensitive data analysis, we reviewed and applied recently developed methods such as classification and regression tree analysis (CART) and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). Findings An intersectional perspective on representativeness unravelled that study participation of women and men might be differential according to further social categories such as civil status or educational level. CART analysis might help to identify intersectional groupings differing in health behaviours or outcomes by exploring a multitude of social dimensions without facing the risk of stereotyping with predefined categories. MAIHDA depicts an alternative method that is suited for descriptive analyses of health-related outcomes among intersectional strata. In contrast to analysing supposedly static features such as sex, a focus on solution-linked variables like social support might be a fertile ground to identify areas for public health action. Discussion Principles of intersectionality open up new perspectives for recruitment and data analysis that might be fruitful for population health research and ultimately for PHMR. Greta Bauer Schulich School of Medicine & Dentistry, Western University, London, Canada Contact: gbauer@uwo.ca Olena Hankivsky University of Melbourne, Centre for Health Equity, Melbourne, Australia Institute for Intersectionality Research, School of Public Policy, Simon Fraser University, Burnaby, Canada Contact: o.hankivsky@unimelb.edu.au Nicole Rosenkötter NRW Centre for Health, Division of Health Reporting, Bielefeld, Germany Contact: Nicole.Rosenkoetter@lzg.nrw.de
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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 | 0.701 | 0.697 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.031 | 0.065 |
| Open science | 0.021 | 0.035 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".