Applying “Acting Within Contexts” to intervention evaluation and equitable intervention design
Bibliographic record
Abstract
Abstract With the rise of chronic illness in the last century, notably diabetes, cardiovascular disease, and obesity, public health has worked to identify the causes of these diseases in order to develop effective interventions and improve population health. Research has shown that these chronic diseases are strongly linked to lifestyle-related risk factors such as smoking, poor eating habits, and physical inactivity. Based on this knowledge, public health interventions have focused on health information and education programs that target these lifestyle-related risk factors. However, these programs have been found to be ineffective because these lifestyle-related strategies do not take into account the contexts that influence behaviour change. This has been found to be particularly true for vulnerable populations who tend to live in contexts of poverty. We therefore asked: how do contexts of vulnerability influence the way people act in their daily lives? To address this question, findings from a qualitative project led to the development of the “Acting Within Contexts” framework. It is a system of five interrelated components (i.e. agency, resources, capacities, threats, and opportunities), which offers an analytical lens to better understand the influence of vulnerable contexts on how people carry out their daily actions. This presentation will first propose an explanation of the limits of public health interventions that aim to change behaviours. Secondly, based on the two previous presentations, it will present how “Acting Within Contexts” can be used: 1) to develop a better understanding of contexts in which vulnerable populations live, based on how the five components interact in people's lives to either hinder or facilitate their access to health; and 2) to design interventions that will shift the focus away from lifestyle-related risk factors, and rather, towards targeting the five components that make up people's everyday contexts.
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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 | 0.305 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".