Barriers to Entry: Socioeconomic Discrepancies Between Unmatched First-Time Applicants and Reapplicants in the Field of Orthopaedic Surgery
Bibliographic record
Abstract
INTRODUCTION: Orthopaedic surgery remains a competitive surgical subspecialty with more applicants than spots each year. As a result, numerous students fail to match into these competitive positions each year with a growing number of reapplicants in consecutive application cycles. We sought to understand the socioeconomic factors at play between this growing reapplicant pool compared with first-time applicants to better understand potential discrepancies between these groups. Our hypothesis is that reapplicants would have higher socioeconomic status and have less underrepresented minority representation compared with successful first-time applicants. METHODS: A retrospective review of deidentified individual orthopaedic surgery applicant data from the American Association of Medical Colleges was reviewed from 2011 to 2021. Individual demographic and application data as well as self-reported socioeconomic and parental data were analyzed using descriptive and advanced statistics. RESULTS: Of the 12,112 applicants included in this data set, 77% were first-time applicants (61% versus 17% successfully entered into an orthopaedic surgery residency vs versus unmatched, respectively), whereas 22% were reapplicants. In successful first-time applicants, 12% identified as underrepresented minorities in medicine. The proportion of underrepresented minorities was significantly higher among unmatched first-time applicants (20%) and reapplicants (25%) ( P < 0.001). Reapplicants (mean = $83,364) and unmatched first-time applicants (mean = $80,174) had less medical school debt compared with first time applicants (mean = $101,663) ( P < 0.001). More than 21% of reapplicants were found to have parents in healthcare fields, whereas only 16% of successful first-time applicants and 15% of unsuccessful first-applicants had parents in health care ( P < 0.001). CONCLUSIONS: Reapplicants to orthopaedic surgery residency have less educational debt and are more likely to have parental figures in a healthcare field compared with first-time applicants. This suggests the discrepancies in socioeconomic status between reapplicants and first-time applicants and the importance of providing resources for reapplicants.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".