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Record W2096013178 · doi:10.1177/1049732303259802

Facilitators and Challenges to Organizational Capacity Building in Heart Health Promotion

2004· article· en· W2096013178 on OpenAlexaffabout
Christine Joffres, Stephanie Heath, Jane Farquharson, Kari Barkhouse, Celeste Latter, David R. MacLean

Bibliographic record

VenueQualitative Health Research · 2004
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsSimon Fraser UniversityHeart and Stroke FoundationDalhousie University
Fundersnot available
KeywordsCapacity buildingAction researchData collectionPromotion (chess)Participatory action researchCitizen journalismHealth promotionOrganization developmentProcess (computing)Organizational learningOrganizational commitmentBusinessPublic relationsKnowledge managementPsychologyNursingSociologyMedicinePolitical sciencePublic healthComputer sciencePedagogySocial science

Abstract

fetched live from OpenAlex

The authors describe the facilitators and challenges to a multi-sectoral initiative aiming at building organizational capacity for heart health promotion in Nova Scotia, Canada. The research process was guided by participatory action research. The study included 21 organizations from diverse sectors. Participant selection for the data collection was purposive. The authors collected data through organizational reflection logs and one-to-one semistructured interviews and used grounded theory techniques for the data analyses. Factors influencing organizational capacity for heart health promotion varied, depending on the project stage. Nonetheless, leadership, organizational readiness, congruence, research activities, technical supports, and partnerships were essential to capacity-building efforts. Approaches to organizational capacity building should be multi-leveled, because organizations are influenced by multiple social systems that are not all equally supportive of capacity.

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)0.054
metaresearch head score (Gemma)0.063
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.016
Scholarly communication0.0130.005
Open science0.0030.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.693
GPT teacher head0.645
Teacher spread0.047 · 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

Citations76
Published2004
Admission routes2
Has abstractyes

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