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Record W2128924276 · doi:10.1093/heapro/dat008

Exploring the process of capacity-building among community-based health promotion workers in Alberta, Canada

2013· article· en· W2128924276 on OpenAlexaffabout
Genevieve Montemurro, Kim D. Raine, Candace I. J. Nykiforuk, Maria Mayan

Bibliographic record

VenueHealth Promotion International · 2013
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsProvincial Laboratory of Public HealthUniversity of Alberta
Fundersnot available
KeywordsCapacity buildingPromotion (chess)SustainabilityProcess (computing)Health promotionProcess managementBusinessFocus groupQualitative researchCoding (social sciences)Knowledge managementInformation exchangeGrounded theoryPublic relationsData collectionNursingMedicineComputer sciencePolitical scienceMarketingSociologyPublic health

Abstract

fetched live from OpenAlex

Community capacity-building is a central element to health promotion. While capacity-building features, domains and relationships to program sustainability have been well examined, information on the process of capacity-building as experienced by practitioners is needed. This study examined this process as experienced by coordinators working within a community-based chronic disease prevention project implemented in four communities in Alberta (Canada) from 2005-2010 using a case study approach with a mixed-method design. Data collection involved semi-structured interviews, a focus group and program documents tracking coordinator activity. Qualitative analysis followed the constant comparative method using open, axial and selective coding. Quantitative data were analyzed for frequency of major activity distribution. Capacity-building process involves distinct stages of networking, information exchange, partnering, prioritizing, planning/implementing and supporting/ sustaining. Stages are incremental though not always linear. Contextual factors exert a great influence on the process. Implications for research, practice and policy are discussed.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.207
GPT teacher head0.413
Teacher spread0.206 · 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

Citations12
Published2013
Admission routes2
Has abstractyes

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