Work for the Prison Service: selected health consequences – investigating the role of personal resources, job demands, work stress, and burnout
Bibliographic record
Abstract
OBJECTIVES: The specific job demands of the Prison Service (PS) may affect the health of officers. The job demands-resources model (JD-R) model was used to design a survey of the consequences of working subject to particular job demands. The aim was to gain an insight into the relationship between job demands, personal resources, occupational stress and burnout and selected health consequence indicators (such as behaviors associated with the consumption of alcohol, stress symptoms). MATERIAL AND METHODS: (AUDIT) and a form with a respondent's particulars. Path analysis using partial least squares structural equation modelling (PLS-SEM) was performed. RESULTS: The assumed hypotheses were partially confirmed by the results. Out of 4 job demands categories only work pace turned out not to be a significant predictor of burnout and stress. For alcohol related behaviors, stress level was the only significant predictor, both as a direct and indirect effect taking into account job demands. It transpired that support from superiors rather than support from colleagues or self-efficacy was a significant moderator in the emotional demands - stress relationship. Limitations of the study and perspectives for its continuation are also presented herein. CONCLUSIONS: Based on the obtained results it may be concluded that job demands and support from superiors do have an impact on stress in the PS group. This is also consistent with available reports in literature. At the same time stress is a significant predictor of alcohol related behaviors. Coping through the use of psychoactive substances was not a significant factor in statistical analyses and it has still not been subject to sufficient scientific analysis. Int J Occup Med Environ Health. 2023;36(6):744-60.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".