Nurse Practitioners and Men’s Primary Health Care
Bibliographic record
Abstract
Though life expectancy sex differences are decreasing in many Western countries, men experience higher mortality rates at all ages. Men are often reluctant to seek medical care because health help-seeking is strongly linked to femininity, male weakness, and vulnerability. Many men are also more likely to access emergency care services in response to injury and/or severe pain instead of engaging primary health care (PHC) services. Nurse practitioners are well positioned to increase men's engagement with PHC to waylay the pressure on emergency services and advance the well-being of men. This article demonstrates how nurse practitioners can work with men in PHC settings to optimize men's self-health and illness prevention and management. Four recommendations are discussed: (1) leveling the hierarchies, (2) talking it through, (3) seeing diversity within patterns, and (4) augmenting face-to-face PHC services. In terms of leveling the hierarchies nurse practitioners can engage men in effectual health decision making. Within the interactions detailed in the talking it through section are strategies for connecting with male patients and mapping their progress. In terms of seeing diversity with in patterns and drawing on the plurality of masculinities, nurse practitioners are encouraged to adapt a variety of age sensitive assessment tools to better intervene and guide men's self-health efforts. Examples of community and web based men's health resources are shared in the augmenting face-to-face PHC services section to guide the work of nurse practitioners. Overall, the information and recommendations shared in this article can proactively direct the efforts of nurse practitioners working with men.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".