One in seven Swiss physicians has left patient care – results from a national cohort study from 1980–2009
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».