Promoting Men’s Health in a Community
Bibliographic record
Abstract
Background: Physical activity (PA) and healthy lifestyle habits directly influence physical health and reduce the risk for chronic lifestyle diseases. An interdisciplinary, community-based approach, informed by baseline information on PA and lifestyle habits, can play an important role in promoting men’s health. Objectives: The objectives of this study were to determine and describe PA levels and lifestyle habits of a sport and non-sport group of adult males and determine whether an association existed between PA levels and lifestyle habits. Method: A quantitative research design was used in this case-control study. Data was collected using a self-developed demographic questionnaire and two standardised questionnaires, the International Physical Activity Questionnaire—Short Form (IPAQ-SF) and the Belloc and Breslow Seven Lifestyle Habits Questionnaire. Results: A total of 102 males aged between 25 and 35 years were included in this study. The only comorbidity reported was hypertension (5.9%). The self-reported PA levels of 51% of the participants in the sport group were classified as health-enhancing, while 27.5% of the participants in the non-sport group were classified as inactive. The two groups were comparable regarding healthy lifestyle habits, such as eating breakfast, not smoking and minimal alcohol consumption. However, cross tabulation of PA levels with lifestyle habits showed that no participant in the sport group exhibited low levels of PA combined with poor lifestyle habits. Conclusion: The baseline information on PA and lifestyle habits of males obtained in this study supports the suggested strategies for healthcare professionals. Through these strategies, healthcare professionals can support communities to increase their PA levels and educate them on healthy lifestyle habits to reduce the risk of chronic lifestyle diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".