Family physicians' attitudes to and support of research: a mixed methods analysis of physician participation in a study on depression self-care
Bibliographic record
Abstract
Objectives: The recruitment and retainment of family physicians (FPs) in research projects is problematic. Contradictory literature exists as to why this is the case. This thesis uses the specific context of Project DIRECT-sc: Depression Intervention via Referral, Education and Collaborative Treatment – Self-Care to further address this problem. Specifically this study aimed to explore factors that affect FP recruitment and retention within a study examining the implementation of self-care for depression in adults with chronic physical diseases. Methods: Prospective participants were randomly approached using the physician registry of the Collège des Médecins du Québec, specifically targeting FPs in the core of Montreal. A mixed methods study adapting sequential explanatory design was conducted. A quantitative phase, including completion of self-administered structured questionnaires at study enrolment and termination, was followed by a qualitative complementary phase involving either participation in a semi-structured post study focus group or a telephone interview. Results: 59 office-based FPs (predominantly remunerated fee-for-service) were recruited, a recruitment rate of 16.8% of an initial random sample of 375. Factors impacting on FPs' enrolment were past involvement with research projects, interest in the specific topic of mental health care delivery, enthusiasm about supported self-care, collegiality, and credibility of the members of the research team and/or the research institution. 66% of recruited FPs complied to varying extent with patient screening, occurring more often for those with previous research experience or in non-government run solo practices. 63% of FPs returned end of study questionnaires, this being more often likely from those younger than 50 or in government run practices. Conclusions: Successful involvement by FPs to research projects appears not only linked to interest in the research topic, but also to issues of professional relationships. This speaks to a role for departments of family medicine and professional organizations to promote cultures of research and to help institutionalize and validate research within community practices.
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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".