One in seven Swiss physicians has left patient care – results from a national cohort study from 1980–2009
Bibliographic record
Abstract
AIMS OF THE STUDY: Physician shortage is problematic, but the percentage of physicians who left patient care in Switzerland is unclear. We set out to describe this percentage and determine whether gender or language region was associated with leaving patient care. METHODS: We analysed the National Registry (Medreg) of all physicians who graduated between 1980 and 2009 in Switzerland. Based on the last known working status noted in Medreg, physicians were classified as “probably involved in patient care” or “potentially left patient care”. We drew an unrestricted random sample of 250 from each category. We searched professional directories / social media to classify each sample. Those with undetermined status received a questionnaire that asked their working status. We quantified the percentage of physicians who left patient care and used Poisson and Cox regression to determine rates and the association of leaving patient care with gender, language region, and year of graduation. RESULTS: We identified 23,112 living physicians in Medreg in 2015. Of these, 18,406 (79.6%) were probably involved in patient care and 4706 (20.4%) had potentially left patient care. In the random sample of 250 physicians probably involved in patient care, 237 were involved in patient care, 11 had left and the status of 2 was undetermined (0.8%). In the random sample of 250 physicians who had potentially left patient care, 109 were involved in patient care, 109 had left, and the status of 32 was undetermined (12.8%). We estimated that 13.6% of physicians had left patient care (95% confidence interval [CI] 11.1–16.1%). According to the most realistic scenario, the rate of physicians who had left patient care was 1.2 per 100 physicians/year (95% CI 0.9–1.6) for those who had graduated between 1980 and 1994, and 1.8 per 100 physicians/year (95% CI 1.4–2.3) for those who graduated between 1995 and 2009 (adjusted hazard ratio 1.74, 95% CI 1.12–2.71). There was no evidence that the risk of leaving patient care was associated with gender or language region. CONCLUSIONS: Approximately one in seven physicians in Switzerland who graduated between 1980 and 2009 left patient care. Leaving patient care was not associated with gender, but the probability of leaving patient care was increased considerably in physicians who graduated more recently. Interventions that aim at keeping physicians in the work force and encourage their return to practice are sorely needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".