Preference Signaling Survey of Program Directors–After the Match
Bibliographic record
Abstract
INTRODUCTION: The 2022 to 2023 orthopaedic residency cycle implemented a preference signaling program (PSP), allowing applicants to send "signals" to up to 30 programs to demonstrate their genuine interest. With the conclusion of the 2022 to 2023 cycle, the primary purpose of this study was to analyze program director (PD) perceptions of the PSP after the match cycle and provide a retrospective evaluation of the effects of the PSP on the orthopaedic resident selection process. METHODS: A 21-question survey was distributed to 98 PDs (32.7% response rate). Contact information was obtained from a national database. RESULTS: Most respondents (96.9%) participated in the American Orthopaedic Association's PSP. The majority (93.7%) view preference signaling as a positive change. Most PDs (56.2%) reported a decreased number in applications received compared with previous years. Receiving a preference signal was ranked among the most important factors in resident selection, and most PDs agreed that preference signaling should be used to screen applicants (84.4%) and differentiate similar applicants (96.8%). Moreover, 65.6% of PDs indicated that they would not rank or invite applicants to interview without a signal or completion of a formal away rotation. PDs report that in the 2022 to 2023 cycle, 98.5% of applicants who matched at their program had sent a preference signal. DISCUSSION: Preference signaling was one of the most important factors assessed during its inaugural application cycle and is anticipated to remain a key tool for screening and differentiating candidates. Applicants should strategically select signal recipients to enhance their success in the match.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".