Effect of Work-Related Behavior and Experience Patterns on Sleep Quality in Emergency Medical Service Personnel
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the influence of work-related behavior and experience patterns on sleep quality in emergency medical service personnel. METHODS: From the total sample of 508 emergency medical service workers who took part in the Germany-wide online survey, 368 respondents completed the questionnaires on sleep characteristics (Pittsburgh Sleep Quality Index [PSQI]) and work-related behavior and experience pattern. Three hundred sixty-seven of the 368 participants also finished the Regensburg Insomnia Scale. RESULTS: Based on their work-related behavior and experience pattern results, individuals were categorized into one of the four following patterns: two risk patterns (A, B) and two healthy behavior and experience patterns (G, S). Participants that were classified into risk-pattern A and B (33.85%) scored significantly higher in both PSQI and Regensburg Insomnia Scale overall score and all PSQI components implicating a poorer sleep quality. A total of 78.5% of the individuals with pattern A and B were considered bad sleepers whereas only 43.4% of individuals with pattern G and S were scored as bad sleepers. CONCLUSIONS: Work-related behavior and experience patterns showed a strong association to sleep characteristics and may therefore be used to identify appropriate preventative measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".