Factors Influencing Participation in Obstetrics by Obstetrician–Gynecologists
Bibliographic record
Abstract
OBJECTIVE: To examine factors affecting participation in obstetrics among obstetrician-gynecologists and changes in participation over time. METHODS: Using physician billings from Ontario, Canada, from 1992/1993 to 2001/2002, we examined the impact of physician age, gender, practice location, and years of practice on participation in obstetrics with multiple logistic regression and repeated measures analyses. We also examined differences in practice patterns between obstetrics providers and nonproviders using linear and log-linear regressions. RESULTS: Obstetrics participation declined with age, from 96% among physicians under age 35, to 34% among those aged 65 and over (2001/2002 figures). Regressions demonstrated a lower likelihood of performing obstetrics in successive years (odds ratio [OR] 0.95 per year; 95% confidence interval [CI] 0.93, 0.98) and among physicians who were older (OR 0.91 per year of age; 95% CI 0.90, 0.93), female (OR 0.57; 95% CI 0.36, 0.91), and practicing in cities with medical schools (OR 0.58; 95% CI 0.44, 0.78). The crude obstetrics participation rate dropped from 82% to 77%, from 1992/1993 to 2001/2002. The physician age-sex-adjusted participation rate dropped from 80% in 1992/1993 to 77% in 2001/2002. Obstetrics providers had almost double the annual billings of nonproviders ($364,000 verus $187,000; P <.001), but more on-call days worked (105 versus 13; P <.001). Nonproviders of obstetrics derived more of their billings from outpatient visits, psychotherapy, and diagnostic tests. The likelihood of an obstetrics nonprovider resuming obstetrics was 1.1% per year. CONCLUSION: The proportion of obstetrician-gynecologists practicing obstetrics declined modestly in the last decade, partly because of more female physicians in the workforce who were less likely to practice obstetrics. Planners should consider these trends when estimating how many obstetrician-gynecologists to train to meet future societal needs. LEVEL OF EVIDENCE: II-2
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".