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Record W2078534807 · doi:10.1177/1757975913490428

Stimulating innovative research in health promotion

2013· review· en· W2078534807 on OpenAlexafffund
Annie Larouche, Louise Potvin

Bibliographic record

VenueGlobal Health Promotion · 2013
Typereview
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversité de MontréalInstitute of Population and Public HealthInstitute of Health Services and Policy Research
FundersCanadian Institutes of Health ResearchPublic Health Agency
KeywordsReflexivityConceptualizationOperationalizationHealth promotionPromotion (chess)Public relationsContext (archaeology)SociologyPolitical scienceMedicineEngineering ethicsPublic healthNursingSocial scienceComputer scienceEpistemologyEngineering

Abstract

fetched live from OpenAlex

The Global Working Group on Health Promotion Research (GWG HPR) of the International Union for Health Promotion and Education (IUHPE) presents a collection of four articles illustrating innovative avenues for health promotion research. This commentary synthesizes the contributions of these articles while attempting to define the contours of research in health promotion. We propose that innovation in research involves the adoption of a reflexive approach wherein consideration of context plays different roles. The reflexive process consists of questioning what is taken for granted in the conceptualization and operationalization of research. It involves linking research findings and its theoretical foundations to characteristics and goals of the field and observed realities, while orienting reflection on specific objects. The reflexive nature of the research activity is of paramount importance for innovation in health promotion. With the publication of this series, the GWG HPR wishes to strengthen health promotion research capacity at the global level and reaffirm health promotion as a specific research domain.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.007
Science and technology studies0.0020.008
Scholarly communication0.0090.011
Open science0.0020.007
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.549
GPT teacher head0.673
Teacher spread0.124 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2013
Admission routes2
Has abstractyes

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