Assessment of the work environment of faculty of a Medical College in Pakistan.
Bibliographic record
Abstract
BACKGROUND: Extensive research is done on nursing work environments but less is known about the job conditions and environments of other health professionals. This study was aimed to fill this information gap by highlighting the factors affecting the work environment and stressors causing turnover of staff. METHODS: A cross sectional study was conducted in Bolan Medical College Quetta for the assessment of working environment of the faculty from 22nd April to 22nd July 2012. All permanent teaching staff was included. A structured questionnaire was adopted fromI health sciences association of Alberta (HSSA), 2006 work Environment Survey. An observational check list for assessment of the physical environment /infrastructure and other general physical stuff was used. RESULTS: The faculty menibers were-not-satisfied with the security and safety of their work place but were satisfied with salaries, employer, and management. Work teams and relationship between employees and employers were respectful with good communication. Majority found their work times stressful and opportunities for on job trainings and professional development, adequate tools, equipment and conditions were mostly lacking. CONCLUSIONS: The overall working environment is not that good and few areas need serious attention like: professional development, trainings, adequate equipment, and security.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".