Predictors of Ophthalmology Resident Research Engagement
Bibliographic record
Abstract
Purpose United States Ophthalmology Residency programs employ many strategies to foster resident academic productivity. The present study assesses the efficacy of these initiatives in promoting research engagement. Methods A 19-question electronic survey was administered to current PGY2 - PGY4 U.S. ophthalmology residents. Primary outcome measures included peer-reviewed publications and national conference presentations while in residency. Results Eighty-one ophthalmology residents completed the survey, including trainees across all ages, genders, and regions. Of respondents, 34.5% reported allocated research time, 96.3% a required research project, 32.1% a research mentor, and 27.1% a formal research curriculum. Only 7% of respondents had not yet published in residency, while the majority authored one (42%) or 2-3 (30%) peer reviewed publications. Predictors for PRPs included PGY year (p = 0.003), pre-residency PRPs (p = 0.018), required research presentation or project (p = 0.05) and post-residency plans to pursue an academic career track (p = 0.036). When excluding case reports, none of the variables were associated with increased academic productivity. A majority (79%) of respondents presented at a national conference during residency. Only pre-residency PRPs predicted national conference participation (p = 0.005). Program allotment of dedicated research time, research mentorship and lecture curriculum did not correlate with an increase in productivity. Conclusions The study herein suggests that PGY year, pre-residency PRPs, research project requirements and academic career aspirations predict increased research productivity; however, dedicated research time, assigned research mentors, and research curriculums do not. These findings may be considered by ophthalmology residency programs when developing curricula to promote academic productivity.
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 | 0.005 | 0.039 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".