Representation of women's health in general medical versus women's health specialty journals: a content analysis
Bibliographic record
Abstract
BACKGROUND: Women's health, traditionally defined, emphasises reproductive and maternal conditions without consideration of social contexts. Advocates urge a broader conceptualisation. The medical literature influences the definitions and delivery of women's health care. We compared how women's health was represented in leading general medical (GM) versus women's health specialty (WS) journals. METHODS: Original investigations published between January 1 - June 30, 1999 in leading GM (n = 514) and WS (n = 82) journals were compared. Data were collected from 99 GM and 82 WS articles on women's health. Independent reviewers conducted content analyses of sample characteristics, study design, and health topic. Each article was classified as "Traditional" (e.g. menstruation, breast cancer), "Non-traditional" (e.g. abuse, osteoporosis), or "Both." RESULTS: Of the GM articles, 53 (53.5%) focused solely on a traditional women's health topic; half were reproductive and half female cancers. In contrast, 22 (26.8%) WS articles were traditionally focused. A non-traditional topic was the sole focus of 27 (27.3%) GM articles versus 34 (41.5%) WS articles. One-fifth of GM and one-third of WS articles addressed both. RCTs dominated the GM articles, while 40% of WS articles used qualitative or mixed study designs. Leading sources of women's death and disability were not well covered in either type of journal. CONCLUSIONS: Most GM articles drew on a narrow definition of women's health. WS journals provided more balanced coverage, addressing social concerns in addition to "navel-to-knees" women's health. Since GM journals have wide impact, editorial decisions and peer review processes should promote a broader conceptualisation of women's health.
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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 | 0.040 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.047 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".