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Record W4210730780 · doi:10.5539/gjhs.v14n3p1

Differences in Male Climacteric Symptoms by Aging Male’s Symptoms Scale and Coping Strategies with Aging among Rotating Night Shift Workers

2022· article· en· W4210730780 on OpenAlexvenueno aff
Sachiko Kubo, Toshiyuki Yasui, Yukie Matsuura, Masahito Tomotake

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsClimactericCoping (psychology)FeelingMedicinePsychologyGerontologyPsychiatryClinical psychologyMenopauseInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.300
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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