Patient and family involvement in Choosing Wisely initiatives: a mixed methods study
Bibliographic record
Abstract
Abstract Background Patients are important stakeholders in reducing low-value care, yet mechanisms for optimizing their involvement in low-value care remain unclear. To explore the role of patients in the development and implementation of Choosing Wisely recommendations to reduce low-value care and to assess the likelihood that existing patient resources will change patient health behaviour. Methods Three phased mixed-methods study: 1) content analysis of all publicly available Choosing Wisely clinician lists and patient resources from the United States of America and Canada. Quantitative data was summarized with frequencies and free text comments were analyzed with qualitative thematic content analysis; 2) semi-structured telephone interviews with a purposive sample of representatives of professional societies who created Choosing Wisely clinician lists and members of the public (including patients and family members). Interviews were transcribed verbatim, and two researchers conducted qualitative template analysis; 3) evaluation of Choosing Wisely patient resources. Two public partners were identified through the Calgary Critical Care Research Network and independently answered two free text questions “would this change your health behaviour” and “would you discuss this material with a healthcare provider”. Free text data was analyzed by two researchers using thematic content analysis. Results From the content analysis of 136 Choosing Wisely clinician lists, six reported patient involvement in their development. From 148 patient resource documents that were mapped onto a conceptual framework (Inform, Activate, Collaborate) 64% described patient engagement at the level of Inform (educating patients). From 19 interviews stakeholder perceptions of patient involvement in reducing low-value care were captured by four themes: 1) impact of perceived power dynamics on the discussion of low-value care in the clinical interaction, 2) how to communicate about low-value care, 3) perceived barriers to patient involvement in reducing low-value care, and 4) suggested strategies to engage patients and families in Choosing Wisely initiatives. In the final phase of work in response to the question “would this change your health behaviour” two patient partners agreed ‘yes’ on 27% of patient resources. Conclusions Opportunities exist to increase patient and family participation in initiatives to reduce low-value care.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".