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Record W2167941775 · doi:10.1177/2158244012446996

Community Capacity Building for Health

2012· article· en· W2167941775 on OpenAlexaffabout
Martha Traverso-Yépez, Victor Maddalena, William Bavington, Catherine Donovan

Bibliographic record

VenueSAGE Open · 2012
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPublic relationsSituational ethicsInterdependenceFlexibility (engineering)Context (archaeology)Promotion (chess)Health promotionCapacity buildingRelevance (law)CitizenshipSociologyWork (physics)Community organizationPolitical sciencePoliticsHealth careSocial scienceManagementEngineering

Abstract

fetched live from OpenAlex

There is a great deal of literature examining the benefits and relevance of community participation and community capacity building in health promotion and disease prevention endeavors. Academic literature embracing principles and commitment to community participation in health promotion practices often neglects the complexities involved and the flexibility required to work within this approach. This article addresses some of these challenges through a case study of two projects funded by Provincial Wellness Grants in Newfoundland and Labrador, a province in Canada with a strong tradition of community ties and support systems. In addition to addressing the unique circumstances of the community groups, this research allowed the authors to examine the situational context and power relations involved in the provision of services as well as the particular forms of subjectivity and citizenship that the institutional practices support. Recognizing this complex interdependency is an important step in creating more effective intervention practices.

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)0.012
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.020
Scholarly communication0.0090.006
Open science0.0020.021
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0440.003

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 head0.389
GPT teacher head0.558
Teacher spread0.169 · 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 designQualitative
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

Citations27
Published2012
Admission routes2
Has abstractyes

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