Strategies to engage family physicians in primary care research: A systematic review
Bibliographic record
Abstract
RATIONALE: Moving towards high quality primary health care, involving family physicians in primary care research becomes an essential prerequisite to ensures a better adoption and routinization of patient-centred, evidence-based practices. AIM: To assess the effectiveness of strategies to engage family physicians in primary care research. METHODS: We systematically reviewed evidence for strategies used to engage family physicians in primary care research. We included any study design that reported at least one quantitative outcome. Searches were carried out on MEDLINE, Embase, PsycINFO and Web of Science. Pairs of reviewers independently screened for publications in two stages using standardized forms. We performed data analysis through a narrative synthesis approach, using the Reasoned-action approach as framework. RESULTS: A total of 4859 deduped records were identified of which 41 studies met the eligibility criteria and were included for analysis. The majority of studies (n = 35) investigated family physician's participation in a research project. They aimed to influence family physicians' intention (n = 7) or their ability (n = 3) to participate in a research project. Three types of strategies (compensation/incentive, recruitment by a peer and support from a research network or an academic institution) demonstrated a significant increase in participation rate. Methodological quality of the studies evaluating these strategies was relatively low. Few studies (n = 6) targeted research capacity-building programmes with no significant impact noted. CONCLUSION: Numerous strategies have been used to engage family physicians in primary care research, but few studies evaluated their effectiveness in a rigorous way. REGISTRATION: The protocol of this review was registered with the SPOR Evidence Alliance and on the PROSPERO platform (registration number: CRD42020189322).
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 | 0.077 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".