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Record W4390941166 · doi:10.17269/s41997-023-00846-6

The Qanuilirpitaa? 2017 Nunavik Health Survey: design, methods, and lessons learned

2024· article· en· W4390941166 on OpenAlexafffundvenueabout
Pierre Ayotte, Susie Gagnon, Mylène Riva, Gina Muckle, Denis Hamel, Richard E. Bélanger, Christopher Fletcher, Christopher Furgal, Aimée Dawson, Chantal Galarneau, Mélanie Lemire, Marie-Josée Gauthier, Elena Labranche, Lucy Grey, Marie Rochette, Françoise Bouchard

Bibliographic record

VenueCanadian Journal of Public Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsNunavik Regional Board of Health and Social ServicesTrent UniversityMcGill UniversityThe Quebec Population Health Research NetworkUniversité LavalInstitut National de Santé Publique du Québec
FundersSentinelle Nord, Université LavalMinistère de la Santé et des Services sociaux
KeywordsThematic analysisEnvironmental healthMental healthPopulationGeographyMedicinePsychologyQualitative researchSociologySocial science

Abstract

fetched live from OpenAlex

OBJECTIVE: To depict the design, methods, sociodemographic characteristics of the population, and lessons learned during the Qanuilirpitaa? 2017 Nunavik Inuit Health Survey, the third major health survey to be conducted among youth and adults residing in Nunavik (Northern Quebec, Canada). METHODS: Qanuilirpitaa? 2017 is a cross-sectional survey that served to update information regarding various aspects of physical health, mental health, and general well-being of Nunavimmiut. The survey was guided by the ethics principles of Ownership, Control, Access, and Possession (OCAP®) ( https://fnigc.ca/ocap ). Questionnaires and clinical tests were administered to residents from the 14 coastal communities onboard the Canadian Coast Guard Ship Amundsen during late summer and early fall 2017. As part of the community component of the survey, qualitative interviews were performed with key respondents, and services and resources supporting health and well-being in the 14 communities were inventoried and characterized. RESULTS: A total of 1326 Nunavimmiut aged 16 and over participated in the survey. Despite difficulties encountered with the recruitment of participants, co-interpretation sessions with Inuit partners revealed that the survey had succeeded in capturing cultural, socio-economic, and lifestyle characteristics of Nunavimmiut. In all, 20 thematic reports have been published covering various aspects of health and well-being of Nunavimmiut. Regional and local reports pertaining to the community component were produced. More in-depth analyses have ensued, and results are presented in articles published in this CJPH supplement issue. CONCLUSION: Information from this survey is being used to update health services and programs in the region and for the development of health policies and public health interventions to tackle key health-related issues faced by Nunavimmiut. Drawing lessons from challenges and successes encountered in Qanuilirpitaa? 2017, this survey paved the way to the upcoming Inuit-led Qanuippitaa? National Inuit Health Survey to be conducted every 5 years throughout Inuit Nunangat.

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)0.037
metaresearch head score (Gemma)0.021
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: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.370
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.384
GPT teacher head0.526
Teacher spread0.142 · 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
GenreMethods

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
Published2024
Admission routes4
Has abstractyes

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