Nature of Job and Psychiatric Problems: The Experiences of Industrial Workers
Bibliographic record
Abstract
AIM: The present study aimed to examine the effect of nature of job (High risk/low risk) on psychiatric problems of 200 workers of Tata Motors Ltd. in Jamshedpur. The workers/participants were divided on the basis of the nature of their job (high/low risk) and their salary (high/low paid) resulting in four sub-groups with 50 participants respectively s. METHODS: The Middlesex Hospital Questionnaire (M.H.Q) constructed by Crown and Crisp (1966) and adapted in Hindi by Srivastava and Bhat in 1974 was administered on the participants. RESULTS: Results clearly indicated that nature of job (high and low risk) played a significant role in creating psychiatric problems in workers. Workers doing high risk jobs showed a greater amount of psychiatric problems compared to workers doing low risk jobs in both high paid and low paid categories. Psychiatric problems included free-floating anxiety, obsessional traits and symptoms, phobic anxiety, somatic concomitants of anxiety, neurotic depression, and hysterical personality traits were seen more in high risk job workers. CONCLUSIONS: High risk job workers had significantly higher psychiatric problems compared to low risk job workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".