Scaling up complex interventions: insights from a realist synthesis
Bibliographic record
Abstract
Preventing chronic diseases, such as cancer, cardiovascular disease and diabetes, requires complex interventions, involving multi-component and multi-level efforts that are tailored to the contexts in which they are delivered. Despite an increasing number of complex interventions in public health, many fail to be 'scaled up'. This study aimed to increase understanding of how and under what conditions complex public health interventions may be scaled up to benefit more people and populations.A realist synthesis was conducted and discussed at an in-person workshop involving practitioners responsible for scaling up activities. Realist approaches view causality through the linkages between changes in contexts (C) that activate mechanisms (M), leading to specific outcomes (O) (CMO configurations). To focus this review, three cases of complex interventions that had been successfully scaled up were included: Vibrant Communities, Youth Build USA and Pathways to Education. A search strategy of published and grey literature related to each case was developed, involving searches of relevant databases and nominations from experts. Data extracted from included documents were classified according to CMO configurations within strategic themes. Findings were compared and contrasted with guidance from diffusion theory, and interpreted with knowledge users to identify practical implications and potential directions for future research.Four core mechanisms were identified, namely awareness, commitment, confidence and trust. These mechanisms were activated within two broad scaling up strategies, those of renewing and regenerating, and documenting success. Within each strategy, specific actions to change contexts included building partnerships, conducting evaluations, engaging political support and adapting funding models. These modified contexts triggered the identified mechanisms, leading to a range of scaling up outcomes, such as commitment of new communities, changes in relevant legislation, or agreements with new funding partners.This synthesis applies and advances theory, realist methods and the practice of scaling up complex interventions. Practitioners may benefit from a number of coordinated efforts, including conducting or commissioning evaluations at strategic moments, mobilising local and political support through relevant partnerships, and promoting ongoing knowledge exchange in peer learning networks. Action research studies guided by these findings, and studies on knowledge translation for realist syntheses are promising future directions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.145 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".