Taking account of context in population health intervention research: guidance for producers, users and funders of research
Bibliographic record
Abstract
Population health intervention research (PHIR) seeks to develop and evaluate policies, programmes and other types of interventions that may affect population health and health equity. Such interventions are strongly influenced by context – taken to refer to any feature of the circumstances in which an intervention is conceived, developed, implemented and evaluated. Understanding how interventions relate to context is critical to understanding how they work; why they sometimes fail; whether they can be successfully adapted, scaled up or translated from one context to another; why their impacts vary; and how far effects observed in one context can be generalised to others. Concerns that context has been neglected in research to develop and evaluate population health interventions have been expressed for at least 20 years. Over this period, an increasingly comprehensive body of guidance has been developed to help with the design, conduct, reporting and appraisal of PHIR. References to context have become more frequent in recent years, as interest has grown in complex and upstream interventions, systems thinking and realist approaches to evaluation, but there remains a lack of systematic guidance for producers, users and funders of PHIR on how context should be taken into account. This document draws together recent thinking and practical experience of addressing context within PHIR. It provides a broad, working definition of context and explains why and how context is important to PHIR. It identifies the dimensions of context that are likely to shape how interventions are conceptualised, the impacts that they have and how they can be implemented, translated and scaled up. It suggests how context should be taken into account throughout the PHIR process, from priority setting and intervention development to the design and conduct of evaluations and reporting, synthesis and knowledge exchange. It concludes by summarising the key messages for producers, users and funders of PHIR and suggesting priorities for future research. The document is meant to be used alongside existing guidance for the development, evaluation and reporting of population health interventions. We expect the guidance to evolve over time, as practice changes in the light of the guidance and experience accumulates on useful approaches. The work was funded by the Canadian Institutes of Health Research (www.cihr-irsc.gc.ca) – Institute of Population and Public Health (CIHR-IPPH) and the UK National Institute for Health Research (NIHR).
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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 | 0.538 | 0.696 |
| Meta-epidemiology (narrow) | 0.007 | 0.011 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.037 | 0.059 |
| Open science | 0.020 | 0.028 |
| Research integrity | 0.046 | 0.055 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".