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Record W2947171998 · doi:10.32799/ijih.v14i1.31932

Building on Strengths: Collaborative Intergenerational Health Research with Urban First Nations and Métis Women and Girls

2019· article· en· W2947171998 on OpenAlexafffundvenueabout
Elizabeth Cooper, S. Michelle Driedger, Josée G. Lavoie

Bibliographic record

VenueInternational Journal of Indigenous Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of ManitobaUniversity of the Fraser Valley
FundersUniversity of Toronto
KeywordsParticipatory action researchIndigenousGeneral partnershipNegotiationAction researchHarmCitizen journalismAction (physics)PsychologyPublic relationsSociologySocial psychologyPolitical scienceSocial sciencePedagogyEcology

Abstract

fetched live from OpenAlex

Little research has focused on how Indigenous girls and their familial female caregivers negotiate issues pertaining to wellbeing and decision-making practices. To address this gap, we employed a novel intergenerational Indigenous partnership methods using various decolonizing action and arts-based activities, to allow participants to guide and modify the direction of the research throughout data collection. We report on three separate activities: a physical game to address concepts of wellness, a memory game that focused on harm reduction and an art project that explored self-esteem. Within each of these activities, female family members and girls worked together to unpack issues of importance within their lives. We conclude that a flexible participatory research design within an intergenerational setting can meet not only the proposed research objectives, but participants’ ever-changing questions and concerns pertaining to health and wellbeing, while still producing rich data to answer important research questions.

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)0.047
metaresearch head score (Gemma)0.025
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 score0.993
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0140.010
Scholarly communication0.0060.008
Open science0.0030.018
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.309
GPT teacher head0.619
Teacher spread0.310 · 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

Citations5
Published2019
Admission routes4
Has abstractyes

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