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Record W2029708668 · doi:10.1177/1757975910394035

Evidence-based health promotion: an emerging field

2011· article· en· W2029708668 on OpenAlexaffabout
Carl-Étienne Juneau, Catherine M. Jones, David V. McQueen, Louise Potvin

Bibliographic record

VenueGlobal Health Promotion · 2011
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPublic relationsAcknowledgementHealth promotionEvidence-based practiceCharterPsychological interventionPromotion (chess)Relevance (law)Political scienceMedicineNursingPoliticsPublic healthAlternative medicine

Abstract

fetched live from OpenAlex

There is much debate around the use of evidence in health promotion practice. This article aims to sharpen our understanding of this matter by reviewing and analyzing the 26 case studies presented in this special issue. These case studies suggest that health promotion practitioners are using a wide range of research evidence in interventions for high-risk individuals, entire populations, and vulnerable groups according to all five strategies for action described in the Ottawa Charter for Health Promotion. In nearly every case, practitioners had to mediate and adapt research evidence for their case. Eight key levers helped practitioners embed research evidence into practice: local and cultural relevance of the evidence, community capacity-building, sustained dialogue from the outset with all stakeholders, established academic-supported partnerships, communication that responds to organizational and political readiness, acknowledgement and awareness of gaps between evidence and practice, advocacy, and adequate earmarked resources. These case studies provide some evidence that there is an evidence-based health promotion, that this evidence base is broad, and that practitioners use different strategies to adapt it for their case.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.421
GPT teacher head0.527
Teacher spread0.106 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2011
Admission routes2
Has abstractyes

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