Designing a tool to capture skill acquisition in serious illness conversations among family physicians: a mixed methods study
Bibliographic record
Abstract
Context: A large team of family medicine, palliative, and end-of-life care clinicians, researchers, and educators from across Canada have collaborated to develop and pilot an innovative serious illness educational program called “All providers: Better Communication Skills (ABCs) Program.” This program is intended to increase family physicians’ competency and comfort with serious illness conversations. Objective: To design a tool capturing skill acquisition in serious illness conversations in family medicine settings. Study Design and Analysis: A Bayesian validation design was adopted using an interviewer-administered mixed-method survey to elicit expert endorsement and perceptions around quality of items. Quantitative data were analyzed descriptively and used to establish Bayesian prior distributions. Qualitative data were analyzed using inductive content analysis. Setting or Dataset: National cohort of experts that are collaborating with the ABCs Program. Population Studied: Nine experts from the cohort. Intervention/Instrument: Relevant items (n=28) from six validated instruments were pooled and slightly revised for improved relevance to the ABCs program. Outcome Measures: Quantitative results and qualitative findings were integrated to guide the removal of unnecessary items and adjust the phrasing of retained items. Results: On average, participants rated items as having moderate-to-high quality (mean=0.7; range=0-1), though there was considerable heterogeneity across raters (standard deviation: 0.27). Fourteen items showed peak prior distributions less than the sample’s average alpha threshold (0.625), indicating experts’ hesitancy in endorsing these items. Four main categories of recommendations were identified including: change wording/phrasing (often to address ambiguity), include an opening prompt (to ensure preceptors using the tool are on the same page about overarching concepts), consider double-barreledness (to ensure only one concept is being evaluated per item), and remove irrelevant item (as concepts seemed well captured by other preferred items). Following this assessment process, team consensus supported the removal of 15 items. Conclusions: We designed and established content validity for a new tool assessing serious illness conversation skills. This tool will be deployed in a pilot trial for further validation with family medicine learners and practitioners, focusing on criterion validity, inter-rater and -item reliability.
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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 | 0.093 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".