Health promotion innovations scale up: combining insights from framing and actor-network to foster reflexivity
Bibliographic record
Abstract
There are numerous hurdles down the road for successfully scaling up health promotion innovations into formal programmes. The challenges of the scaling-up process have mainly been conceived in terms of available resources and technical or management problems. However, aiming for greater impact and sustainability involves addressing new contexts and often adding actors whose perspectives may challenge established orientations. The social dimension of the scaling-up process is thus critical. Building on existing conceptualizations of interventions as dynamic networks and of evolving framing of health issues, this paper elaborates a social view of scaling up that accounts for the transformations of innovations, using framing analysis and the notion of 'expanding scaling-up networks'. First, we discuss interventions as dynamic networks. Second, we conceptualize scaling-up processes as networks in expansion within which social learning and change occur. Third, we propose combining a 'representational approach' to frame analysis and an 'interactional approach' that illustrates framing processes related to the micro-practices of leading public health actors within expanding networks. Using an example concerning equity in early childhood development, we show that this latter approach allows documenting how frames evolve in the process. Considering the process in continuity with existing conceptualizations of interventions as actor-networks and transformation of meanings enriches our conceptualization of scaling up, improves our capacity to anticipate its outcomes, and promotes reflexivity about health promotion goals and means.
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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 | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".