The Choice! The challenges of trying to improve medical students’ satisfaction with their specialty choices
Bibliographic record
Abstract
The authors describe the residency match as a two-step process. The first step, the Choice, is where students use a combination of intuitive and analytic information processing to select the specialty that they believe will provide fulfilment and work-life balance over their entire career. The second step, the Match, uses a “deferred-acceptance” algorithm to optimize pairing of students and their specialty choices. Despite being the rate-limiting step, outcomes of the Choice have typically been eclipsed by the outcomes of the Match. A recently published study found that during their second year of residency training, 1 in 14 physicians reported specialty choice regret, which associates with symptoms of burnout in residents. While the obvious solution is to design interventions that improve the specialty choices of students, this approach faces significant challenges, including the fact that: 1) satisfaction with specialty choice is a difficult-to-define construct; 2) specialty choice regret may be misattributed to a poor choice; and 3) choosing is a more complicated process than matching. The authors end by suggesting that if we hope to improve satisfaction with specialty choice then we should begin by defining this, deciding when to assess it, and then creating assessment tools for which there is validity evidence and that can identify the underlying causes of specialty choice regret.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".