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Record W4392558072 · doi:10.1177/11771801241235373

Mental health implementation research in Indigenous communities: creating culturally safe space to enhance collective strengths

2024· article· en· W4392558072 on OpenAlexaffabout
Nicole D’souza, Michaela Field, Tristan Supino, Mia Messer, Erin Aleck, Laurence J. Kirmayer

Bibliographic record

VenueAlterNative An International Journal of Indigenous Peoples · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsMcGill UniversityJewish General HospitalPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsIndigenousSpace (punctuation)Mental healthSociologyPsychologyPublic relationsEngineering ethicsPolitical scienceComputer scienceEngineeringPsychiatryEcology

Abstract

fetched live from OpenAlex

In this article, we discuss the construct of cultural safety in relation to the ethics, politics, and practice of implementation research in Indigenous communities. We convened a 3-day workshop, bringing together 23 Indigenous and non-Indigenous collaborators from First Nation communities and universities across Canada to reflect on experiences with implementing an Indigenous youth and family mental health promotion program in First Nation communities. Participants identified three dimensions central to achieving culturally safe space in implementation research: (1) interpersonal dynamics of collaboration between Indigenous and non-Indigenous partners; (2) structural and temporal arrangements necessary for collaborative work; and (3) the systematic recognition and incorporation of Indigenous cultural knowledge, values, and practices. Within implementation research, attention to cultural safety can mitigate the epistemic injustice that arise from research frameworks and methodologies that exclude Indigenous perspectives and values. Cultural safety can increase the likelihood that the research process itself contributes to mental health promotion.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.041
GPT teacher head0.474
Teacher spread0.434 · 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 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAlterNative An International Journal of Indigenous PeoplesSame topicIndigenous Health, Education, and RightsFrench-language works237,207