Who self‐cares wins: a global perspective on men and self-care
Bibliographic record
Abstract
In this report, ‘men’s health’ is used as shorthand for ‘the health and wellbeing of men and boys’. The term ‘health practice’ is preferred to the more commonly-used concept of ‘health behaviour’. In many ways, the terms are synonymous but ‘health practice’ suggests that what people ‘do’ is not simply a matter of individual decision but is also influenced by a range of wider social, economic and cultural factors.1 Comparisons between men’s health and women’s health should not be read as meaning that women’s health is unproblematic or that bringing men’s outcomes into line with women’s would be sufficient. That is far from the case – women’s health also needs significant attention. But because the differences in male and female health are only to a small degree inevitable (less than 1-2 years of the life expectancy ‘gap’ is believed to be genetically determined)2, they are one useful indicator of where action is needed. A limitation of this report is that it is based on evidence published in English and focused primarily on the Global North which covers about one-quarter of the world’s population and includes the United States of America, Australia, New Zealand, Canada and Europe. Information relevant to the Global South (broadly including Africa, Latin America, and the developing countries in Asia), has been included wherever possible, however. All figures cited have been rounded for the purposes of clarity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".