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Record W4383212617 · doi:10.54488/ijcar.2022.305

The Development of the First Nations Children Wellbeing Measure

2022· article· en· W4383212617 on OpenAlexafffundvenueabout
Alexandra S. Drawson, Elaine Toombs, J. Bruce Blain, Tina Bobinski, John Dixon, Natalie Paavola, Christopher J. Mushquash

Bibliographic record

VenueInternational Journal of Child and Adolescent Resilience · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsLakehead University
FundersCanadian Institutes of Health ResearchPublic Health Agency of Canada
KeywordsIndigenousPsychologyCitizen journalismFocus groupWell-beingPrincipal (computer security)TreatyApplied psychologyPublic relationsMedical educationSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Objectives: The overall goal of this project was to implement a measurement tool intended to assess the wellbeing of First Nations children within the Robinson Superior Treaty Area.
 Methods: A community-based participatory research approach was utilized, which included a research advisory composed to employees from the organization that was partnered with. Both interviews and focus groups were held with members of the communities within the Robinson Superior Treaty Area, and the content of these was examined to determine indicators of wellbeing for children in these communities. The indicators that arose from this analysis made up the pilot version of the measure, which was administered to the parents or caregivers of children (n = 91) who were seen through the intake service for the organization. They were also administered the Child and Adolescent Needs and Strengths measure (Lyons et al., 1999).
 Results: A principal components analysis was performed, which yielded three factors: (1) General Wellbeing, (2) Traditional Activities, and (3) Social Engagement.
 Discussion: Involvement in traditional activities and engagement in culture were cited as fundamental indicators of wellbeing of First Nations children, which is consistent with the majority of the literature. The instrument that was created and evaluated represents one of few valid tools available to assess this.
 Implications: The measure and process of creating this measure contributes to the literature on the significance of traditional activities for the wellbeing of Indigenous people.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.334
Teacher spread0.316 · 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.

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
Published2022
Admission routes4
Has abstractyes

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