Teaching Shared Decision Making to Family Medicine Residents: A Descriptive Study of a Web-Based Tutorial
Bibliographic record
Abstract
BACKGROUND: DECISION+2, a Web-based tutorial, was designed to train family physicians in shared decision making (SDM) regarding the use of antibiotics for acute respiratory infections (ARIs). It is currently mandatory for second-year family medicine residents at Université Laval, Quebec, Canada. However, little is known about how such tutorials are used, their effect on knowledge scores, or how best to assess resident participation. OBJECTIVE: The objective of our study was to describe the usage of this Web-based training platform by family medicine residents over time, evaluate its effect on their knowledge scores, and identify what kinds of data are needed for a more comprehensive analysis of usage and knowledge acquisition. METHODS: We identified, collected, and analyzed all available data about participation in and current usage of the tutorial and its before-and-after 10-item knowledge test. Residents were separated into 3 log-in periods (2012-2013, 2013-2014, and 2014-2015) depending on the day of their first connection. We compared residents' participation rates between entry periods (Cochran-Armitage test), assessed the mean rank of the difference in total scores and category scores between pre- and posttest (Wilcoxon signed-rank test), and compared frequencies of each. Subsequent to analyses, we identified types of data that would have provided a more complete picture of the usage of the program and its effect on knowledge scores. RESULTS: The tutorial addresses 3 knowledge categories: diagnosing ARIs, treating ARIs, and SDM regarding the use of antibiotics for treating ARIs. From July 2012 to July 2015, all 387 second-year family medicine residents were eligible to take the Web-based tutorial. Out of the 387 eligible residents, 247 (63.8%) logged in at least once. Their participation rates varied between entry periods, most significantly between the 2012-2013 and 2013-2014 cohorts (P=.006). For the 109 out of 387 (28.2%) residents who completed the tutorial and both tests, total and category scores significantly improved between pre- and posttest (all P values <.001). However, the frequencies of those answering correctly on 2 of the 3 SDM questions did not increase significantly (P>.99, P=.25). Distribution of pre- or posttest total and category scores did not increase between entry periods (all P values >.1). Available data were inadequate for evaluating the associations between the tutorial and its impact on the residents' scores and therefore could tell us little about its effect on increasing their knowledge. CONCLUSION: Residents' use of this Web-based tutorial appeared to increase between entry periods following the changes to the SDM program, and the tutorial seemed less effective for increasing SDM knowledge scores than for diagnosis or treatment scores. However, our results also highlight the need to improve data availability before participation in Web-based SDM tutorials can be properly evaluated or knowledge scores improved.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".