A systematic process for creating and appraising clinical vignettes to illustrate interprofessional shared decision making
Bibliographic record
Abstract
Vignettes and written case simulations have been widely used by educators and health services researchers to illustrate plausible situations and measure processes in a wide range of practice settings. We devised a systematic process to create and appraise theory-based vignettes for illustrating an interprofessional approach to shared decision making (IP-SDM) for health professionals. A vignette was developed in six stages: (1) determine IP-SDM content elements; (2) choose true-to-life clinical scenario; (3) draft script; (4) appraise IP-SDM concepts illustrated using two evaluation instruments and an interprofessional concept grid; (5) peer review script for content validity; and (6) retrospective pre-/post-test evaluation of video vignette by health professionals. The vignette contained six scenes demonstrating the asynchronous involvement of five health professionals with an elderly woman and her daughter facing a decision about location of care. The script scored highly on both evaluation scales. Twenty-nine health professionals working in home care watched the vignette during IP-SDM workshops in English or French and rated it as excellent (n = 6), good (n = 20), fair (n = 0) or weak (n = 3). Participants reported higher knowledge of IP-SDM after the workshops compared to before (p < 0.0001). Our video vignette development process resulted in a product that was true-to-life and as part of a multifaceted workshop it appears to improve knowledge among health professionals. This could be used to create and appraise vignettes targeting IP-SDM in other contexts.
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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 | 0.189 | 0.309 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".