Who Leaves Orthopedics? Analyzing Attrition Among Texas Orthopedic Surgeons
Notice bibliographique
Résumé
Background: Nationally, attrition rates in surgical specialties have been reported to be as high as 20-26%. As of 2019, women comprised only 6.5% of the 29,613 orthopedic surgeons. Female residents showed significantly higher overall attrition rates compared to males (5.96% vs. 2.79%, p < 0.001), with unintended attrition nearly twice as high (2.09% vs. 1.01%, p < 0.001). Attrition can result from a wide array of variables, including sex, ethnicity, education, and background. The aim of this study was to evaluate the demographic and educational factors associated with career longevity beyond training among orthopedic surgeons, with a focus on identifying patterns of attrition in underrepresented groups. Methods: Data from the Texas Medical Board Physician Database as of September 2024 was evaluated using Microsoft Excel. To determine length of practice, the date of license issuance was subtracted from the date of license expiration. Surgeons with license expirations after 2024 were excluded to maintain a focus on physicians that are no longer practicing. Entries with missing or blank data fields were excluded from the analysis. Demographic variables, including sex, medical degree type (MD v. DO), and medical school origin (International Medical Graduate (IMG) vs. U.S. medical graduate (US, Canada, Puerto Rico) were reviewed and were examined. Results: This study examined factors influencing orthopedic surgeons’ career longevity, with comparisons made using chi-square analysis. Key variables included gender, race, and educational background. Attrition was highest in the first 10 years post-licensure across all demographics. Only 10% of surgeons in this range were non-white minorities. Female surgeons had a 10-year attrition rate of 80.8% (p < 0.001), compared to 40.9% (p = 0.154) for males. White surgeons displayed no significant deviations across practice duration, especially in the first ten years of practice (884 observed vs. 913 expected, p=0.332). Minorities exhibited greater attrition earlier in their careers (Asian, p < 0.001; Black, p = 0.007). This trend reversed after 20 years when comparing IMGs at 19.4% (p < 0.001) versus U.S. graduates at 12.2% (p = 0.280). Conclusion: Female orthopedics experience disproportionately high attrition rates within the first 10 years of practice, underscoring systemic challenges such as family planning pressures, workplace discrimination and lack of support. Similarly, racial minorities show high early-career attrition rates, pointing to barriers such as discrimination and isolation within the field. IMG surgeons exhibited lower long-term attrition rates than their U.S. counterparts, potentially reflecting the resilience cultivated through more challenging training and licensure pathways. This study is limited by its inclusion of recent graduates who may inflate attrition rates and its lack of data on geographic and employment transitions. Addressing these gaps could provide a more comprehensive understanding of the factors influencing career longevity.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».