Psychosocial Risk Factors and Self-Reported Health of the Medical Personnel in the Polyclinic of Petropavlovsk (Kazakhstan)
Bibliographic record
Abstract
Abstract Objectives:Psychosocial risk factors at work in developing countries still stay without due research cognizance. This study aims to measure psychosocial risks in the work of medical workers in Kazakhstan. Methods: Job Content Questionnaire (JCQ) by Karasek and colleagues was applied in this survey, modified by the authors in accordance to the study objectives, mainly for studying the relation between psychosocial risks and health of medical employees. The medical staff was categorized to nine occupational groups based on the International Standard Classification of Occupations (ISCO-08). Data was analyzed statistically with the use of the IBM Statistics SPSS 21 program. Findings: High strained job is performed only by nurse`s aides of the polyclinic. High strain is mainly explained by extreme workloads and lack of decisional latitude. This is hardly to be compared to the outcomes in any other previous studies since there is no specification for nurse`s aides at all. General practitioners encounter higher strain at work than specialist doctors. Although the specification of doctors is extremely important is ignored in the most of research. Social support in the polyclinic is low in comparison to those values received in other studies. Meanwhile, social support is salient to employees in decreasing their job dissatisfaction. Overall, non-material incentives are more important for health workers to feel satisfied. Job dissatisfaction is also affected by the changes in the working position. Alteration in the job rank was included into the questionnaire as an additional scale. The poor health status is mainly professed by employees of elementary occupations. Similarly, workers holding lowerranks in previous investigations report health status more negative. Improvements/Applications: This study is devoted to psychosocial risks at work of medical personnel in Kazakhstan. Results of the research, though, are applicable in developing countries undergoing transformation, particularly in healthcare. Keywords decisional latitude, job dissatisfaction, job strain, occupational groups, psychological demands, social support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".