Modes of delivery in preventive intervention studies: a rapid review
Bibliographic record
Abstract
BACKGROUND: This review was commissioned to generate broad discussion about how to select intervention delivery modes when designing a complex, preventive intervention aimed at chronic disease through the promotion of physical activity, healthy diet and/or medication adherence. In this context, we asked, what are the delivery modes? What are the important design considerations? And how do these compare (e.g. strengths, limitations)? MATERIALS AND METHODS: This review utilized the methods of rapid review, an emerging methodology arising from health technology assessment. The search strategy was applied in Embase and MEDLINE. A qualitative, narrative synthesis was performed on included articles. RESULTS: After screening, 21 articles remained for synthesis (10 systematic reviews, including 1 review of reviews; four trials or studies; three commentaries or conference proceedings; and 2 were scoping projects). Our synthesis determined that major categories of design considerations when selecting intervention delivery modes include attention to the (i) candidate mode types, (ii) settings and social environment, (iii) intensity and timing, (iv) provider, (v) study population and participants, (vi) cost, (vii) behaviour change technique and (viii) theoretical basis. CONCLUSION: An array of modes of delivery is available for each of the intervention strategies under consideration (i.e. physical activity, dietary change and medication adherence). No single delivery mode was clearly more appropriate or more effective than another, each having unique strengths and limitations. Delivery mode decisions that take the above-mentioned factors (i-viii) into account will be more fit-for-purpose than those that do not.
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 | 0.160 | 0.375 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".