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Record W6888570090 · doi:10.20381/ruor-29498

Community Food Security and Wellbeing: Evaluation of the Nutrition North Canada Cooking Circle Program in Paulatuk, NWT

2023· article· en· W6888570090 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorParticipatory action researchFood securityConceptualizationLogic modelQualitative researchProgram evaluationProcess (computing)Citizen journalism

Abstract

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Limited research exists on the role of community food programs, such as cooking circles, in Canadian Arctic communities. This research investigates the Nutrition North Canada cooking circle program in the Inuvialuit (Inuit) hamlet of Paulatuk, NWT. The objectives are to 1) collaboratively evaluate the program's impact on healthy food intake, and awareness/knowledge accumulation regarding healthier eating and meal preparation techniques; 2) assess the program's intangible (e.g., social, cultural, and mental health) benefits to participants and community wellbeing; and 3) analyze the potential for consistent country food integration; 4) to generate a culturally relevant logic model; 5) to evaluate program implementation in terms of key enablers and barriers - namely, funding, space and equipment, and human resources. This research applies a Community-Based Participatory Research approach to implementing a collaborative process and outcome program evaluation, with necessary modifications due to the Covid-19 pandemic. Methods include logic model co-design, document analysis of NNC annual reports (n = 9) and proposals (n = 2), a series of regular/weekly iterative videoconference conversations with the Paulatuk cooking circle facilitator out of which two were transcribed and analyzed (n = 2), videoconference semi-structured interview with a regional program manager (n = 1), telephone semi-structured interviews with cooking circle facilitators from other communities (n = 4), telephone (n = 1) and in-person (n = 12) semi-structured interviews with Paulatuk cooking circle participants. The Mukluk Logic Model played an instrumental role in the conceptualization of the process evaluation, recognizing the specificity of location, program facilitation, and culture. The process evaluation results indicated that the long-standing sustainability of Paulatuk's cooking circle program is directly related to the consistency of program funding, community-engaged facilitation practices, and creative utilization of the community's multi-purpose space for program activities. However, significant barriers to program implementation limit program sustainability. These include funding amounts and distribution, limitations related to space and equipment, and a challenging human resources context (hiring and retention). The cooking circle participants reported an increase in intake of healthy food, awareness of healthy eating/cooking, and positive social and mental health aspects of the program. A considerable effort from local and regional stakeholders is required to integrate country food into program activities. This study provides insight into cooking circle program implementation in a Canadian Arctic community, illustrating the utility of qualitative process evaluation research in this context and generating important knowledge and insights to better support northern community-based food preparation programs. Additionally, this research highlights the role of local-scale initiatives in supporting food security, overall wellbeing, and integration of country food in program activities in remote, Arctic communities.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.136
GPT teacher head0.404
Teacher spread0.267 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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