Obstetrics and gynecology clerkship directors’ experiences advising residency applicants
Bibliographic record
Abstract
BACKGROUND: The evolving landscape of application processes for obstetrics and gynecology residency applicants poses many challenges for applicants and advisors. The lack of data coordination among national groups creates crucial gaps in information for stakeholder groups. OBJECTIVE: This study aimed to identify the current state of the advising milieu for obstetrics and gynecology residency applicants and their career advisors, the annual Association of Professors of Gynecology and Obstetrics survey focused on US clerkship directors' experiences advising students through these processes. STUDY DESIGN: A 23-item anonymous survey was developed that asked respondents about demographics and outcomes for the students that they advised through the 2021 application process and their experiences with dual applicants and students not matching. The survey was sent electronically to all obstetrics and gynecology clerkship directors with active Association of Professors of Gynecology and Obstetrics memberships in April 2021. RESULTS: Of 224 total clerkship directors, 143 (63.8%) responded to the survey, Of the 143 respondents, almost all (136 [95.1%]) served as career advisors, and 50 (35.0%) were aware of students dual applying. Furthermore, obstetrics and gynecology was rarely the backup to a more competitive specialty. For the 2021 application cycle, 79 of 143 respondents (55.2%) reported having students not successfully match into obstetrics and gynecology, with "academic concerns" followed by "poor communication skills" as the primary reasons cited for students not matching. CONCLUSION: This snapshot of clerkship directors' experiences advising students in the residency application process reveals notably high rates of dual applicants and students not matching into obstetrics and gynecology. This work fills key gaps in our knowledge of current processes and highlights the importance of career advising at multiple points during the application process.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".