The Case for Retaining a Focus on “Masculinities” in Men’s Health Research
Bibliographic record
Abstract
Within the health research literature there is increasing attention focussed on how \nthe concept of “masculinities” can be employed to understand health and illness \nand used to inform health care practice and policy. At the same time, valuable \ncritiques of masculinities frameworks have emphasised that there is often, within \nthe published literature, a lack of rigour in defining and using these ideas, a tendency \ntowards rigid and essentialist notions about men and gender but also recognition \nthat some approaches specify masculinities as the “cause” of poor health \noutcomes for men, women and children. We consider and respond to these important \nquestions and, using examples from empirical studies, make the case that \nit is important to advance the use of masculinities in men’s health research both \nas a means to describing the challenges to men’s health and the strengths men \ndraw upon to promote their health and remedy illness. We argue, first, that masculinities \nbe operationalised as “configurations of social practice” and understood \nas part of the dynamic processes involved within the “gender order.” \nSecond, configurations of social practice are diverse, dynamic and hierarchical \nin terms of the material and representational benefits they bring to men. Third, \nconfigurations of social practice are relational and negotiated within institutions \nand other structures wherein the doing of masculinities and health and illness can \nbe co-constructed, contested and/or constrained. Finally, we suggest some practice \nimplications and applications for further conceptualising masculinities to the \nfield of men’s health. \n
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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 | 0.183 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.138 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".