Bibliographic record
Abstract
Low-value care consists of medical tests and treatments that are unnecessary, potentially harmful, or not cost-effective and contribute to rising healthcare costs, adverse events, and poor quality of care. In recent years there has been a surge in initiatives aiming to identify and reduce low-value care. However, the role of the public in reducing low-value care remains unclear. The research reported in this thesis aimed to understand the role of the public in reducing low-value care through a systematic and comprehensive review of the literature. A scoping review identified 151 relevant articles. The majority of these articles described or evaluated a strategy for involving the public in reducing low-value care; articles that explored stakeholder perspectives about the role of the public were less common. Public involvement most commonly occurred at the level of the patient-clinician interaction, followed by administrative and policy decision-making and low-value care research. Shared decision-making and patient-oriented education were the most frequent and best supported strategies. There was considerably less support for public involvement at the level of administrative and policy decision-making. A follow-up systematic review and meta-analysis was conducted to estimate the impact of patient-targeted interventions to reduce low-value care. This study found a statistically significant association between patient-targeted interventions (i.e., shared decision-making, patient-oriented education) and a decrease in use of the low-value practices (RR 0.75; 95% CI 0.66-0.84), which remained significant when the meta-analysis was restricted to randomized clinical trials with low risk of bias (RR 0.69; 95% CI 0.58-0.83). Collectively, these two studies show a considerable amount of support for engaging the public in reducing low-value care at the level of the patient-clinician interaction through strategies including shared decision-making and patient-oriented education. There is comparably less evidence to support public involvement in research or administrative and policy decision-making. Additional research to explore stakeholder perspectives and evaluate strategies for public involvement within varying contexts is required to further understand the role of the public in reducing 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 | 0.060 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".