Differences in Male Climacteric Symptoms by Aging Male’s Symptoms Scale and Coping Strategies with Aging among Rotating Night Shift Workers
Bibliographic record
Abstract
The aim of this study was to clarify how male rotating night shift workers cope with male climacteric symptoms and whether coping strategies are different depending on age. A self-administered questionnaire survey regarding coping strategies in male rotating night shift workers over the age of 20 years was performed. Male climacteric symptoms were evaluated by using the Aging Male’s Symptoms scale [AMS]. Of 1,891 questionnaires that were sent, 1,561 were collected. For all of the 16 symptoms, the most frequent strategy was to try to ignore and tolerate the symptoms and the second-most frequent strategy was to take time to relax. The proportions of men who ignored and tolerated psychological symptoms and sleep problems were high in all age groups. The proportions of men who ignored and tolerated the symptom of decline in the feeling of general well-being were high in men in their 20s and low in men in their 60s. The proportion of men who consulted a doctor for the symptom of joint and muscular pain was high in men in their 50s. The most frequent strategy for coping with male climacteric symptoms was to ignore and tolerate the symptoms and the second-most frequent strategy was to take time to relax. There was a difference in coping behavior depending on age in rotating night shift workers.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".