What Factors Are Associated with the Research Productivity of Primary Care Researchers in Canada? A Qualitative Study
Bibliographic record
Abstract
Context: Research evidence to inform primary care (PC) policy and practice is essential for building high-performing PC systems. Nevertheless, research output relating to PC remains low worldwide. Objective: To examine the individual, professional, institutional and system factors that influence the research productivity (RP) of PC researchers. Study Design and Analysis: We used a qualitative, descriptive key informant study approach to conduct semi-structured interviews with senior, mid-career and early-career PC researchers across Canada. Qualitative data were analyzed using reflexive thematic analysis. Setting: Canada. Population Studied: PC researchers. Intervention/Instrument: Semi-structured interviews. Outcome Measures: Qualitative perceptions, experiences, opinions, and beliefs. Results: Twenty-three PC researchers participated in the study. An interplay of factors was perceived to enable or reduce RP. Facilitators of RP included personal (psychological characteristics, spousal occupation, and support), professional (mentorship before faculty appointment), institutional (mentorship, institutional type), and system (international collaborations) factors. Barriers to RP included personal (parenthood, gender, race, and educational background) and system (systematic bias, environment) factors. Professional (national collaborations, research expertise, and length of career), institutional (leadership, culture, resources, protected time), and system (funding for PC research, geography, research data infrastructure) factors served as barriers or facilitators. Conclusion: Funders and academic institutions are critical in supporting and accelerating RP. At the institutional level, departments should recruit leaders committed to PC research who will cultivate a supportive, flexible, and equitable culture for researchers. This should be accompanied by investments in research and administration, dedicating protected time, and formalizing mentorship programs. Trainees and early-career faculty should choose to work in supportive working environments and seek out different mentors and colleagues that will respectfully support their career goals. At the systems level, governments and granting agencies should consider targeted funding for PC research and training and support a national data infrastructure to enable the continuous flow of PC research. At all levels, strategies must be implemented to address gender and racial inequalities.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".