P.104 Factors influencing resident engagement in research during post-graduate training
Bibliographic record
Abstract
Background: Residency training programs aspire to develop residents’ research skills, but engaging trainees in research often proves challenging. Addressing this requires a better understanding of factors influencing residents’ engagement in scholarship. We sought to identify such factors through an interview-based study that explored residents’ interest and involvement in research during training. Methods: We conducted 15 semi-structured interviews with neurology (n=8) and neurosurgery (n=7) residents at our institution based on an interview guide developed through a literature review and pilot interviews (n=3). Using template analysis, we examined transcripts to identify facilitators and barriers to resident research. Results: Motivation, mentorship, and resource availability were noted to significantly impact resident research. Trainees indicated motivation is influenced by personal desire to develop research skills, interest in available projects, and pressure to engage in scholarship from peers, mentors, and future employers. While strong mentorship and departmental resources for data collection and analysis facilitate resident research, funding and time constraints are barriers to success. Conclusions: We have identified multiple factors influencing residents’ engagement in research, which may be targeted by program directors to optimize the post-graduate training environment for resident scholarship. In the next phase of our project, we will corroborate and expand on these findings through a national survey of residents across all specialties.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".