Bibliographic record
Abstract
The purpose of this study was to determine the current state of self-reported sleep quality in workers of the mining industry and identify the factors that affect sleep in this sample. A large-scale questionnaire was administered to 2,224 workers of the mining industry with operations in Ontario. A modified version of the Pittsburgh Sleep Quality Index (mPSQI) was used to determine sleep quality and quantity. A total of 84% of participants self-reported poor sleep quality with an average mPSQI score of 6.43 (± 3.07). The average sleep duration of participants was 6hr:05min (± 1hr:03min), which is lower than the recommended 7-8 hrs of sleep. Participants engaging in hazardous drug and alcohol use, screening positive for mental health concerns, stress and fatigue, experiencing workplace burnout and working shifts, self-reported worst sleep quality. Finally, depression, personal burnout, fatigue, PTSD, shift work, diagnosis of a chronic disease and hazardous drug use were significant predictors of poor subjective sleep quality, accounting for 37.1% of the total variance of sleep quality (R²= 0.371, F(7, 1572) = 131.78, p ≤ 0.000). These data will assist in developing targeted strategies and interventions for workers to achieve better sleep quality, overall well being and a safer workplace.
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 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 | 0.000 | 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) | 0.001 | 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".