Gender and help-seeking: towards gender-comparative studies
Bibliographic record
Abstract
The question of whether there are differences in the use that men and women make of healthcare services has occupied researchers for many decades (see, for example, Cleary et al., 1982; Mechanic, 1976; Nathanson, 1977). It is often taken as a given that men make lesser use of healthcare services than women (see, for example, White and Witty, 2009). Statements such as that ‘men are less likely than women to actively seek medical care when they are ill, choosing instead to “tough it out”’ (Tudiver and Talbot, 1999: 47) are common. Men’s supposed ‘underuse’ or delayed use of healthcare is often taken to be a key part of the explanation for men’s shorter life expectancy in comparison with women (White and Witty, 2009). Their underuse of the healthcare system is constructed as a social problem (O’Brien et al., 2005: 503) and has moved up the policy agenda in countries such as the UK, the USA, Australia and Canada in recent years (see also Chapter 14 by Schofield).
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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 | 0.054 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".