How do financial (dis)incentives influence health behaviour and costs? Protocol for a systematic literature review of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: In this era of rising healthcare costs, there is a growing interest in understanding how funding policies can be used to improve health and healthcare efficiency. Financial incentives (eg, vouchers or access to health insurance) or disincentives (eg, fines or out-of-pocket costs) affect behaviours. To date, reviews have explored the effects of financial (dis)incentives on patient health and behaviour by focusing on specific behaviours or geographical areas. The objective of this systematic review is to provide a comprehensive overview on the use of financial (dis)incentives as a means of influencing health-related behaviour and costs in randomised trials. METHODS AND ANALYSIS: We will search electronic databases, clinical trial registries and websites of health economic organisations for randomised controlled trials. The initial searches, which were conducted on 13 January 2018, will be updated every 12 months until the completion of data analysis. The reference lists of included studies will be manually screened to identify additional eligible studies. Two researchers will independently review titles, abstracts and full texts to determine eligibility according to a set of predetermined inclusion criteria. Data will be extracted from included studies using a form developed and piloted by the research team. Discrepancies will be resolved through discussion with a third reviewer. Risk of bias will be assessed using the Cochrane Collaboration tool. ETHICS AND DISSEMINATION: Ethics approval is not required since this is a review of published data. Results will be disseminated through publication in peer-reviewed journals and presentations at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42018097140.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.181 |
| Meta-epidemiology (narrow) | 0.011 | 0.010 |
| Meta-epidemiology (broad) | 0.035 | 0.023 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.090 | 0.018 |
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".