Understanding decisions to scale up: a qualitative case study of three health service intervention evaluations
Bibliographic record
Abstract
OBJECTIVE: Efforts to scale up evidence-based health care interventions are seen as a key strategy to address complex health system challenges. However, scale-up efforts have shown significant variability. We address the gap between scale-up theory and practice by exploring the socio-cultural factors at play in the evaluation and scale-up of three interventions within the clinical field. METHODS: A qualitative multiple case study was conducted to characterize the evaluation and scale-up efforts of three interventions. We interviewed 18 participants, including clinicians and researchers across the three cases. Using Pierre Bourdieu's concepts of field and capital as a theoretical lens, we conducted a thematic analysis of the data. RESULTS: Despite the espoused goals of ensuring that health service interventions are always based on high-quality evidence within the clinical field, this study demonstrates that the outcomes of the evaluations are not the only factor in the decision to engage in scale-up efforts. Important socio-cultural factors also come into play. Bourdieu uses the term capital to refer to the resources that agents compete for and with their acquisition, accumulate power and/or social standing. The type of evidence valued in the clinical field and the ability to leverage capital in demonstrating that value are also important factors. CONCLUSIONS: Determining if an intervention is effective and should be scaled up is more complex in practice than described in the literature. Efforts are needed to explicitly include the role of social processes in the current frameworks guiding scaling-up efforts.
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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 | 0.092 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".