Exploring the predictive effects of masculinity on Black/African American men's preventive health care utilization
Bibliographic record
Abstract
Background: Numerous studies have confirmed men, their gender norms associated with masculinity, and health are interrelated concepts that influence men's health-seeking behaviours and outcomes. Using the tenets of the Andersen Health care Utilization Model, this study examines predisposing, enabling, and constraining factors along with masculinity as predictors of preventive health care utilization in Black/African American (BAA) men. Public health nurses (PHNs), in knowing the importance of preventive measures, can play a pivotal role in improving BAA men's health outcomes. Methods: This nonexperimental correlational cross-sectional design included 176 BAA men aged 26–74 years from the Upper Midwest metropolitan area answering questions on their demographics, current preventive health care utilization using the Older Men's Health Program and Screening Inventory, and conformity to masculine norms using the Conformity to Masculine Norms Inventory-46. Results: Winning, self-reliance, and heterosexual self-presentation were the three norms that significantly contributed to participants’ preventive health care utilization. Higher conformity to heterosexual self-presentation was associated with less preventive measures utilized within the past year, while higher conformity to winning was associated with higher odds of seeing the doctor within the past year. Higher conformity to self-reliance were associated with higher likelihood of visiting an ER within the past year. Conclusions: With improved investigation and closer attention to perceptions and masculine norm conformity, the health care system would have a better grasp on how to approach and improve health disparities among BAA men. From here, PHNs can better determine what it means to be masculine within the BAA community and how BAA men perceive preventive health care to improve health initiatives geared toward BAA men and decrease identified health disparities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".