Cross-national comparison of job satisfaction in doctors during economic recession
Bibliographic record
Abstract
BACKGROUND: Job satisfaction in doctors is related to migration, burnout, turnover and health service quality. However, little is known about their job satisfaction during economic recessions. Iceland and Norway have similar health care systems, but only Iceland was affected severely by the 2008 economic crisis. AIMS: To examine job satisfaction in Icelandic and Norwegian doctors, to compare job satisfaction with Icelandic data obtained before the current recession and to examine job satisfaction in response to cost-containment initiatives. METHODS: A survey of all doctors working in Iceland during 2010, a representative comparison sample of Norwegian doctors from 2010 and a historic sample of doctors who worked at Landspitali University Hospital in Iceland during 2003. The main outcome measure was job satisfaction, which was measured using a validated 10-item scale. RESULTS: Job satisfaction levels in Icelandic doctors (response rate of 61%, n = 622/1024), mean = 47.7 (SD = 10.9), were significantly lower than those of Norwegian doctors (response rate of 67%, n = 1025/1522), mean = 53.2 (SD = 8.5), after controlling for individual and work-related factors. Doctors at Landspitali University Hospital (response rate of 59%, n = 345/581) were less satisfied during the recession. Multiple regression analysis showed that cost-containment significantly affected job satisfaction (P < 0.001). CONCLUSIONS: Job satisfaction in doctors was lower in Iceland than in Norway, which may have been attributable partly to the current economic recession.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".