It’s Not Rocket Science: The Case from Ireland for a Policy Focus on Men’s Health
Bibliographic record
Abstract
Historically, men, as a population group, have been conspicuous by their absence at a global and national health policy level. Moreover, most gender-focused health policy initiatives and gender-mainstreaming approaches to health have tended to be synonymous with women’s health. This places Ireland’s National Men’s Health Policy (NMHP) and recent external 5-year review in the collector’s item category within the wider health policy landscape. This paper will review the impetus and background to men’s health policy development in Ireland against a backdrop of the invisibility of men more generally from health policy. Reflecting on the key milestones and challenges associated with transitioning from policy development to implementation, the paper will seek to inform a wider public health debate on the case for targeting men as a specific population group for the strategic planning of health. The case for a NMHP on the grounds of a gender inequity will also be explored in the context of contributing more broadly to gender equality. There will be a particular focus on exploring how strategies associated with governance and accountability, advocacy, research and evaluation, partnerships and capacity-building, have acted as a catalyst and framework for action in the rollout of a broad range of men’s health initiatives. With the central challenge being the translation of cross-departmental and inter-sectoral recommendations into sustainable actions, the role of NMHP in applying a gender lens to other policy areas will also be discussed. Ireland’s NMHP has raised the visibility of men’s health in Ireland; the lessons learned during its implementation provide a strong rationale and blueprint for NMHP development elsewhere.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".