Men’s Health in Northern Ireland: Why do we need a men’s health policy?
Bibliographic record
Abstract
Males accounted for half the United Kingdom population in 2021 yet they fail to be prioritised in health and social policies. As examining the health of males and females collectively falls short in accounting for the complexities associated with gendered health outcomes, male health should be considered as a separate policy issue. The island of Ireland has two jurisdictions, the Republic of Ireland and Northern Ireland (NI); however, only the former has implemented a men's health policy. As well as a policy vacuum within NI, few studies have comprehensively examined male health. To address this shortcoming, a narrative review of males' physical and mental health trends in NI is presented to determine the need for a men's health policy. A collation of secondary administrative data and survey data was conducted. The narrative review highlights the importance of utilising a holistic framework to understand men's health. Key findings include high male suicide rates and young males being more likely to report certain mental health problems. The study concludes that a male health policy is needed. To achieve this, a Health Impact Pyramid was developed, and it illustrates practical steps that can be taken to support decision-makers, service providers and individual males.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".