MétaCan
Menu
Back to cohort
Record W2802302644 · doi:10.3310/cihr-nihr-01

Taking account of context in population health intervention research: guidance for producers, users and funders of research

2018· report· en· W2802302644 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of WaterlooUniversity of VictoriaUniversity of OttawaUniversité de MontréalUniversity of British ColumbiaYork UniversityPublic Health OntarioUniversity of Toronto
FundersSchool for Public Health ResearchPublic Health Research ProgrammeEconomic and Social Research CouncilCanadian Institutes of Health ResearchCentre for Diet and Activity ResearchFonds National de la Recherche LuxembourgNational Institute for Health and Care ResearchNational Institutes of HealthInternational Development Research CentreInstitut pour la Recherche en Santé PubliqueDepartment of Health and Social CareGovernment of the United KingdomScottish GovernmentBritish Heart FoundationWellcome TrustUniversity of SouthamptonUnited Kingdom Clinical Research CollaborationUniversity of CambridgeCancer Research UKInstitute of Population and Public HealthMedical Research CouncilLondon School of Hygiene and Tropical Medicine
KeywordsPsychological interventionIntervention (counseling)Equity (law)Affect (linguistics)Context (archaeology)PopulationPsychologyPublic relationsBusinessEnvironmental healthMedicinePolitical scienceNursingGeography

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.551
GPT teacher head0.605
Teacher spread0.055 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations384
Published2018
Admission routes3
Has abstractyes

Explore more

Same topicHealth disparities and outcomesFrench-language works237,207