Developing a framework to inform scale-up success for population health interventions: a critical interpretive synthesis of the literature
Bibliographic record
Abstract
Background: Population health interventions (PHIs) have the potential to improve the health of large populations by systematically addressing underlying conditions of poor health outcomes (i.e., social determinants of health) and reducing health inequities. Scaling-up may be one means of enhancing the impact of effective PHIs. However, not all scale-up attempts have been successful. In an attempt to help guide the process of successful scale-up of a PHI, we look to the organizational readiness for change theory for a new perspective on how we may better understand the scale-up pathway. Using the change theory, our goal was to develop the foundations of an evidence-based, theory-informed framework for a PHI, through a critical examination of various PHI scale-up experiences documented in the literature. Methods: We conducted a multi-step, critical interpretive synthesis (CIS) to gather and examine insights from scale-up experiences detailed in peer-reviewed and grey literatures, with a focus on PHIs from a variety of global settings. The CIS included iterative cycles of systematic searching, sampling, data extraction, critiquing, interpreting, coding, reflecting, and synthesizing. Theories relevant to innovations, complexity, and organizational readiness guided our analysis and synthesis. Results: We retained and examined twenty different PHI scale-up experiences, which were extracted from 77 documents (47 peer-reviewed, 30 grey literature) published between 1995 and 2013. Overall, we identified three phases (i.e., Groundwork, Implementing Scale-up, and Sustaining Scale-up), 11 actions, and four key components (i.e., PHI, context, capacity, stakeholders) pertinent to the scale-up process. Our guiding theories provided explanatory power to various aspects of the scale-up process and to scale-up success, and an alternative perspective to the assessment of scale-up readiness for a PHI. Conclusion: Our synthesis provided the foundations of the Scale-up Readiness Assessment Framework. Our theoretically-informed and rigorous synthesis methodology permitted identification of disparate processes involved in the successful scale-up of a PHI. Our findings complement the guidance and resources currently available, and offer an added perspective to assessing scale-up readiness for a PHI.
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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 | 0.353 | 0.410 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.051 | 0.028 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".