Physicians’ job satisfaction in their begin, mid and end career stage
Bibliographic record
Abstract
Objective: To examine whether physicians differ in job satisfaction in different career stages, controlling for “gender”, “specialty area” and “level of income”.Methods: Survey of three cohorts of physicians who started studying in 1972-75 (n = 704), 1982/83 (n = 301) and 1992/93 (n = 296) at the University of Groningen. Physicians in the begin, mid and end career stage practiced for 10, 20 and 30 years respectively. Data were collected by telephonic interviews and written questionnaires. We selected 13 job satisfaction aspects which could be mapped unto Ostroff’s taxonomy of organizational climate perceptions. Influences of gender, specialty area and level of income were taken into account.Results: Physicians in begin, mid and end career stage differed on eight aspects. Taking into account gender, specialty area and level of income, differences between career stages were significant for three aspects: appreciation from support personnel, appreciation from patients and satisfaction with income. Specialty area was the most important covariate.Conclusions: Physicians from different career stages differed in job satisfaction, but specialty area accounted to a large extent for these differences. We recommend taking into account physicians’ career stage, gender and specialty area when studying physicians’ job satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".