Implementation Research on Shared Decision Making in Primary Care: Inventory of Intracluster Correlation Coefficients
Bibliographic record
Abstract
Background. Cluster randomized trials are important sources of information on evidence-based practices in primary care. However, there are few sources of intracluster correlation coefficients (ICCs) for designing such trials. We inventoried ICC estimates for shared decision-making (SDM) measures in primary care. Methods. Data sources were studies led by the Canada Research Chair in Shared Decision Making and Knowledge Transition. Eligible studies were conducted in primary care, included at least 2 hierarchical levels, included SDM measures for individual units nested under any type of cluster (area, clinic, or provider), and were approved by an ethics committee. We classified measures into decision antecedents, decision processes, and decision outcomes. We used Bayesian random-effect models to estimate mode ICCs and the 95% highest probability density interval (HPDI). We summarized estimates by calculating median and interquartile range (IQR). Results. Six of 14 studies were included. There were 97 ICC estimates for 17 measures. ICC estimates ranged from 0 to 0.5 (median, 0.03; IRQ, 0–0.07). They were higher for process measures (median, 0.03; IQR, 0–0.07) than for antecedent measures (0.02; 0–0.07) or outcome measures (0.02; 0–0.06), for which, respectively, “decisional conflict” (mode, 0.48; 95% HPDI, 0.39–0.57), “reluctance to disclose uncertainty to patients” (0.5; 0.11–0.89), and “quality of the decision” (0.45; 0.14–0.84) had the highest ICCs. ICCs for provider-level clustering (median, 0.06; IQR, 0–0.13) were higher than for other levels. Limitations. This convenience sample of studies may not reflect all potential ICC ranges for primary care SDM measures. Conclusions. Our inventory of ICC estimates for SDM measures in primary care will improve the ease and accuracy of power calculations in cluster randomized trials and inspire its further expansion in SDM 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 | 0.473 | 0.631 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".