MétaCan
Menu
Back to cohort
Record W2898594966 · doi:10.32799/ijih.v13i1.30279

Understanding the Sleep Habits of Children Within an Indigenous Community

2018· article· en· W2898594966 on OpenAlexafffundvenue
Richard Hovey, Evangeline Seganathy, Angela C Morck, Morgan Phillips, Adriana Poulette, Morrison King, Ann C. Macaulay, Reut Gruber

Bibliographic record

VenueInternational Journal of Indigenous Health · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsDouglas Mental Health University InstituteMcGill UniversityKahnawake Schools Diabetes Prevention Project
FundersLawson FoundationMcGill University
KeywordsIndigenousSleep (system call)Meaning (existential)Participatory action researchFocus groupPsychologyCitizen journalismCommunity-based participatory researchDevelopmental psychologySociologyGerontologyPedagogyMedicinePolitical sciencePsychotherapistAnthropology

Abstract

fetched live from OpenAlex

This study was developed within the participatory research framework of a diabetes prevention project to understand the meaning of sleep and sleep habits of Indigenous preschool and elementary school children. Sleep deprivation is a known risk factor for obesity and Type 2 diabetes. A philosophical hermeneutic approach utilized interviews and focus groups with cultural knowledge holders, Elders, parents, teachers, and school administrators. The findings reflect how Indigenous community members understood sleep through the themes of traditional ways, changing times and concerns, increasing technology, generation gaps, parental responsibility, eating habits, physical activity, and children’s behaviours in school. After dissemination to the community, the findings were combined with traditional teachings and national recommendations to develop culture- and age-appropriate sleep-promoting educational materials for schools and the broader community.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.353
Teacher spread0.285 · 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 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
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Indigenous HealthSame topicChild Nutrition and Water AccessFrench-language works237,207