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Record W2505121897

Exploring Partnership Functioning Within a Community-Based Participatory Intervention to Improve Disaster Resilience

2016· article· en· W2505121897 on OpenAlexaffabout
Elizabeth Gagnon, Tracey O’Sullivan, Daniel E. Lane, Nicole Paré

Bibliographic record

VenueJournal of higher education outreach & engagement/Journal of higher education outreach and engagement. · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaMontfort Hospital
Fundersnot available
KeywordsCommunity-based participatory researchParticipatory action researchGeneral partnershipPublic relationsCapacity buildingPreparednessCommunity resilienceEmergency managementPsychological resilienceResilience (materials science)Citizen journalismPublic healthSociologyPolitical scienceNursingPsychologyMedicineSocial psychologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Disasters happen worldwide, and it is necessary to engage emergency management agencies, health and social services, and community-based organizations in collaborative management activities to enhance community resilience. Community-based participatory research (CBPR) has been widely accepted in public health research as an approach to develop partnerships between academic researchers and community stakeholders and to promote innovative solutions to complex social issues. Little is known, however, about how CBPR partnerships function and contribute to successful outcomes. In this article, the authors present a case study of a CBPR partnership formed with the community of Quebec City, Canada, under the Enhancing Resilience and Capacity for Health (EnRiCH) Project, to improve emergency preparedness and adaptive capacity among high-risk populations. This qualitative study presents participants’ perspectives on how the partnership functioned and the outcomes of this collaboration. Findings are discussed in relation to contextual and group dynamics, as well as system and capacity outcomes.

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)0.016
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.002
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 head0.580
GPT teacher head0.570
Teacher spread0.010 · 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 designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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