Protocol for two interrelated systematic reviews of multiple health behaviour change interventions in healthcare
Bibliographic record
Abstract
BackgroundMultiple health behaviour change (MHBC) interventions are proposed to be an effective and efficient way of intervening to manage and/or prevent chronic conditions, both with patients (targeting health-related behaviours, e.g. diet, smoking) and healthcare professionals (targeting clinical behaviours, e.g. advice, examine). However, their effectiveness is still unclear. Therefore, a review of these interventions in this context is warranted. Two interrelated systematic reviews of MHBC interventions will be conducted, one targeting health-related behaviours of patients with chronic conditions and another one targeting clinical behaviours of healthcare professionals.Materials and methods These systematic reviews will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic search for MHBC interventions for patients will be performed in Web of Science, PubMed, CINAHL, EMBASE and Cochrane, and a systematic search for healthcare professionals will be conducted in the same databases. Studies from previous reviews will also be consulted. We will include randomised trials, in which interventions aim to change more than one behaviour (health-related for patients, clinical for HCPs). Components of the interventions will be extracted by using existing standardised classifications, such as ontologies (e.g. mode of delivery) and taxonomies (e.g. behaviour change techniques taxonomy v1), and the Cochrane Collaboration revised tool of Risk of Bias will be applied to perform risk of bias assessment. The components of included interventions will be synthesised and links between them will be mapped to identify trends and gaps. If sufficient comparable studies are included, a meta-analysis will be performed in both reviews. Systematic reviews registrationsCRD42022327085 and CRD42022327108
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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 | 0.136 | 0.218 |
| Meta-epidemiology (narrow) | 0.010 | 0.010 |
| Meta-epidemiology (broad) | 0.026 | 0.026 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.187 | 0.038 |
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".