Overview of the Drivers of Low-Value Care Comment on "Key Factors that Promote Low-Value Care: Views of Experts From the United States, Canada, and the Netherlands"
Bibliographic record
Abstract
Verkerk and colleagues explored the key drivers of low-value care from the perspective of 18 policy-makers and researchers who had led and evaluated at least one initiative to reduce low-value care or had been responsible for reducing low-value care in an organisation. They identified several drivers of low-value care presented in the 2017 Lancet Right Care Series (eg, fee for service payment systems, the pharmaceutical and medical device industry, fear of malpractice litigation, issues with research conduct and reporting, a culture of ‘more is better’ and ‘new technology is better’) but did not discuss some other important ones. In this commentary, we aim to extend the work of Verkerk and colleagues and provide some additional perspectives on the drivers of low-value care within the following categories: Economic incentives; Money, finance, and organisation; Knowledge beliefs, assumptions, bias and uncertainty; and Power and human relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".