Family physicians' selection of informal peer consultants: Implications for continuing education
Bibliographic record
Abstract
INTRODUCTION: Studies of physicians' preferred sources of clinical information suggest that many rely heavily on advice from colleagues. This study examines the criteria that a sample of physicians used to select informal educational consultants, the characteristics of participants adhering to these criteria and those of the peers they consult, and the participants' approaches toward evaluating information gathered from peers. METHOD: In-depth interviews were conducted with 45 family physicians from three mid-sized Ontario cities. A typology of participants' approaches for selecting informal peer consultants was developed from participants' selection criteria. Seven themes emerged from analysis across the interviews, and three types of approaches to selecting peer consultants are characterized with respect to these themes. RESULTS: When seeking clinical information, most participants reported that their first resource was informal consultation with peers. Fifty-four percent turned to readily available and approachable peers, and 24% asked only those peers they considered to be experts. The remaining participants (22%) searched the literature before or in conjunction with consulting expert specialists or innovators. Participants who sought advice from their most readily accessible peers asked for advice most frequently, rarely consulted innovators, and were least critical of the advice they received. DISCUSSION: The profiles of those who sought clinical information from their most accessible peers suggested that the quality of informal peer consultations could be improved through explicit guidelines within formal continuing education programs. Longitudinal studies are needed to examine the effectiveness of this strategy in increasing the translation of research into family physicians' clinical practices and patient outcomes.
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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".