Quality of work life in doctors working with cancer patients
Bibliographic record
Abstract
BACKGROUND: Although studies have shown that medical residents experience poor psychological health and poor organizational conditions, their quality of work life (QWL) had not been measured. A new tool, the Quality of Work Life Systemic Inventory (QWLSI), proposes to fill the gap in the definition and assessment of this concept. AIMS: To confirm the convergent validity of the QWLSI, analyse Belgian medical residents' QWL with the QWLSI and discuss an intervention methodology based on the analysis of the QWLSI. METHODS: One hundred and thirteen medical residents participated between 2002 and 2006. They completed the QWLSI, the Maslach Burnout Inventory and the Job Stress Survey to confirm the correspondence between these three tools. RESULTS: Residents' low QWL predicted high emotional exhaustion (β = 0.282; P < 0.01) and job stress (β = 0.370; P < 0.001) levels, confirming the convergent validity. This sample of medical residents had an average QWL (μ = 5.8; SD = 3.1). However, their QWL was very low for three subscales: arrangement of work schedule (μ = 9; SD = 6.3), support offered to employee (μ = 7.6; SD = 6.1) and working relationship with superiors (μ = 6.9; SD = 5.3). CONCLUSIONS: The results confirm that the QWLSI can provide an indication of workers' health well-being and of organizational performance in different areas of work life. The problem factors found among Belgian medical residents suggest that prevention should focus on reduction of work hours, development of support and change in leadership style.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".