Turning Problems into Progress for Primary Care Research Trainees: A Mixed-Methods Analysis of an Online Cross-Sectional Survey
Bibliographic record
Abstract
Background As the field of primary care research continues to grow, it is increasingly important to address the concerns of our trainees. Trainees are central to workforce development and represent the future of the field. Identifying the specific needs and barriers they face in pursuing primary care research is essential to advancing the discipline. Methods In this mixed-methods approach, we analyzed responses to an 33-item cross-sectional survey via REDCap. We performed quantitative analysis using RStudio for Windows (version 2025.05.1) and manually coded overarching themes in Microsoft Excel using an inductive thematic analysis approach. Findings Sixty-nine survey responses were included in the quantitative analysis. A majority of responses were from allopathic medical (MD) students, representing 28.08% of respondents ( n = 18), followed by medical residents ( n = 17; 24.64%). We received responses from four countries: the United States ( n = 45; 65.20%), Netherlands ( n = 12; 17.40%), Canada ( n = 11; 15.90%), and Uganda ( n = 1; 1.40%). We analyzed 66 quotes from 29 participants using an inductive thematic approach, and uncovered nine overarching themes: (1) Guidance and Mentorship, (2) Networking, (3) Training, (4) Time, (5) Funding, (6) Resources, (7) Support, (8) Sustainability, and (9) Institutional Limitations. Conclusion Primary care research trainees face complex challenges such as limited time, funding, mentorship, and research skills, compounded by clinical demands and institutional barriers. Solutions include protected research time, structured mentorship, networking, and equitable institutional support. Future research should identify trainees’ priorities and develop actionable strategies to support primary care research trainees.
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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 | 0.077 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".