What Motivates Family Physicians to Participate in Training Programs in Shared Decision Making?
Bibliographic record
Abstract
INTRODUCTION: Little is known about the factors that influence family physician (FP) participation in continuing professional development (CPD) programs in shared decision making (SDM). We sought to identify the factors that motivate FPs to participate in DECISION+, a CPD program in SDM. METHODS: In 2007-2008, we collected data from 39 FPs who participated in a pilot randomized trial of DECISION+. In 2010, we collected data again from 11 of those participants and from 12 new subjects. Based on the theory of planned behavior, our questionnaire assessed FPs' intentions to participate in a CPD program in SDM and evaluated FPs' attitudes, subjective norms and perceived behavioral control. We also conducted 4 focus groups to explore FPs' salient beliefs. RESULTS: In 2010, FPs' mean intention to participate in a CPD program in SDM was relatively strong (2.6 ± 0.5 on a scale from -3 = "strongly disagree" to +3 = "strongly agree"). Affective attitude was the only factor significantly associated with intention (r = .51, p = .04). FPs identified the attractions of participating in a CPD program in SDM as (1) its interest, (2) the pleasure of learning, and (3) professional stimulation. Facilitators of their participation were (1) a relevant clinical topic, (2) an interactive program, (3) an accessible program, and (4) decision support tools. DISCUSSION: To attract FPs to a CPD program in SDM, CPD developers should make the program interesting, enjoyable, and professionally stimulating. They should choose a clinically relevant topic, ensure that the program is interactive and accessible, and include decision support tools.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".