Understanding the public’s role in reducing low-value care: a scoping review
Bibliographic record
Abstract
BACKGROUND: Low-value care initiatives are rapidly growing; however, it is not clear how members of the public should be involved. The objective of this scoping review was to systematically examine the literature describing public involvement in initatives to reduce low-value care. METHODS: Evidence sources included MEDLINE, EMBASE, and CINAHL databases from inception to November 26, 2019, grey literature (CADTH Tool), reference lists of included articles, and expert consultation. Citations were screened in duplicate and included if they referred to the public's perception and/or involvement in reducing low-value care. Public included patients or citizens without any advanced healthcare knowledge. Low-value care included medical tests or treatments that lack efficacy, have risks that exceed benefit, or are not cost-effective. Extracted data pertained to study characteristics, low-value practice, clinical setting, and level of public involvement (i.e., patient-clinician interaction, research, or policy-making). RESULTS: The 218 included citations were predominantly original research (n = 138, 63%), published since 2010 (n = 192, 88%), originating from North America (n = 146, 67%). Most citations focused on patient engagement within the patient-clinician interaction (n = 156, 72%), using tools that included shared decision-making (n = 66, 42%) and patient-targeted educational materials (n = 72, 46%), and reported both reductions in low-value care and improved patient perceptions regarding low-value care. Fewer citations examined public involvement in low-value care policy-making (n = 33, 15%). Among citations that examined perspectives regarding public involvement in initiatives to reduce low-value care (n = 10, 5%), there was consistent support for the utility of tools applied within the patient-clinician interaction and less consistent support for involvement in policy-making. CONCLUSIONS: Efforts examining public involvement in low-value care concentrate within the patient-clinician interaction, wherein patient-oriented educational materials and shared decision-making tools have been commonly studied and are associated with reductions in low-value care. This contrasts with inclusion of the public in low-value care policy decisions wherein tools to promote engagement are less well-developed and involvement not consistently viewed as valuable. TRIAL REGISTRATION: Open Science Framework (https://osf.io/6fsxm).
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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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".