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Record W2984085371 · doi:10.32799/ijih.v14i2.31910

Exploring the health and well-being of children and youth in Winneway, Québec

2019· article· en· W2984085371 on OpenAlexaffvenueabout
Alison Kutcher, Priscilla Pichette, Mary Ellen Macdonald, Franco A. Carvenvale

Bibliographic record

VenueInternational Journal of Indigenous Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndigenousMental healthPerspective (graphical)Well-beingPsychological interventionPsychologyEthnographyDevelopmental psychologySociologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Health inequalities of Indigenous children and youth in Canada are well documented. Recently, children and youths’ perspectives are being recognized as valuable. However, there is a paucity of literature that seek children and youth’s perspective regarding their health and well-being. The purpose of this study was to understand how children and youth in Winneway, QC view health and well-being and to identify their main health and well-being concerns. A focused ethnographic study with Indigenous decolonizing framework was used with data primarily collected through interviews of fifteen participants aged 6 to 17. Children and youth in Winneway view their health and well-being as multidimensional and view themselves as decision-makers in their health and well-being choices. Their main health and well-being concerns include poor eating choices, difficulty expressing emotional and mental concerns, how children and youth treat others, and youth participation in unhealthy behaviours. These findings reveal the valuable perspectives that Indigenous children and youth can have regarding their health and well-being. They also suggest that future health and well-being interventions targeting Indigenous children and youth seek out and respect the knowledge and perspectives that children and youth have of their health and well-being.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.302
Teacher spread0.280 · 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 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

Citations3
Published2019
Admission routes3
Has abstractyes

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