MétaCan
Menu
Back to cohort
Record W2612417679 · doi:10.1080/22423982.2017.1343637

Participatory methods for Inuit public health promotion and programme evaluation in Nunatsiavut, Canada

2017· article· en· W2612417679 on OpenAlexaffabout
M. Saini

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsParticipatory evaluationStakeholderCitizen journalismIndigenousFocus groupPublic healthHealth promotionCommunity-based participatory researchPublic relationsWhiteboardParticipatory action researchScope (computer science)Stakeholder engagementMedical educationPolitical scienceSociologyMedicineNursingEngineeringComputer science

Abstract

fetched live from OpenAlex

Engaging stakeholders is crucial for health promotion and programme evaluations; understanding how to best engage stakeholders is less clear, especially within Indigenous communities. The objectives of this thesis research were to use participatory methods to: (1) co-develop and evaluate a whiteboard video for use as a public health promotion tool in Rigolet, Nunatsiavut, and (2) develop and validate a framework for participatory evaluation of Inuit public health initiatives in Nunatsiavut, Labrador. Data collection tools included interactive workshops, community events, interviews, focus-group discussions and surveys. Results indicated the whiteboard video was an engaging and suitable medium for sharing public health messaging due to its contextually relevant elements. Participants identified 4 foundational evaluation framework components necessary to conduct appropriate evaluations, including: (1) community engagement, (2) collaborative evaluation development, (3) tailored evaluation data collection and (4) evaluation scope. This research illustrates stakeholder participation is critical to develop and evaluate contextually relevant public health initiatives in Nunatsiavut, Labrador and should be considered in other Indigenous 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 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)0.024
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.848
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.472
GPT teacher head0.602
Teacher spread0.130 · 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.

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

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Circumpolar HealthSame topicCommunity Health and DevelopmentFrench-language works237,207