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Record W2752266489 · doi:10.1177/1539449217727117

Approaches for building community participation: A qualitative case study of Canadian food security programs

2017· article· en· W2752266489 on OpenAlexaboutno aff
Nerida Hyett, Amanda Kenny, Virginia Dickson‐Swift

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisPublic relationsScope (computer science)Qualitative researchFood securityCapacity buildingCommunity buildingOccupational therapySustainabilitySociologyPsychologyPolitical scienceKnowledge managementBusinessMedical educationMedicineComputer scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

There is increasing opportunity and support for occupational therapists to expand their scope of practice in community settings. However, evidence is needed to increase occupational therapists' knowledge, confidence, and capacity with building community participation and adopting community-centered practice roles. The purpose of this study is to improve occupational therapists' understanding of an approach to building community participation, through case study of a network of Canadian food security programs. Qualitative case study was utilized. Data were semistructured interviews, field observations, documents, and online social media. Thematic analysis was used to identify and describe four themes that relate to processes used to build community participation. The four themes were use of multiple methods, good leaders are fundamental, growing participation via social media, and leveraging outcomes. Occupational therapists can utilize an approach for building community participation that incorporates resource mobilization. Challenges of sustainability and social exclusion must be addressed.

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)0.028
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
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 head0.864
GPT teacher head0.688
Teacher spread0.176 · 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 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

Citations15
Published2017
Admission routes1
Has abstractyes

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