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Record W4281734779 · doi:10.21203/rs.3.rs-1713950/v1

Centering Indigenous Knowledge in Suicide Prevention: A Critical Scoping Review

2022· preprint· en· W4281734779 on OpenAlexaffabout
Erynne Sjoblom, Winta Ghidei, Marya Leslie, Ashton James, Reagan Bartel, Sandy Campbell, Stephanie Montesanti

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMétis National CouncilUniversity of Alberta
Fundersnot available
KeywordsIndigenousThematic analysisGrey literatureContext (archaeology)Participatory action researchMental healthPopulationCitizen journalismCommunity-based participatory researchIntervention (counseling)Suicide preventionMedicinePoison controlPolitical scienceQualitative researchMEDLINENursingGeographySociologyPsychiatryEnvironmental healthSocial science

Abstract

fetched live from OpenAlex

Abstract BackgroundIndigenous peoples of Canada, United States, Australia, and New Zealand experience disproportionately high rates of suicide as a result of the collective and shared trauma experienced with colonization and ongoing marginalization. Dominant, Western approaches to suicide prevention—typically involving individual-level efforts for behavioural change via mental health professional intervention—by themselves have largely failed at addressing suicide in Indigenous populations, possibly due to cultural misalignment with Indigenous paradigms. Consequently, many Indigenous communities, organizations and governments have been undertaking more cultural and community-based approaches to suicide prevention. To provide a foundation for future research and inform prevention efforts in this context, this critical scoping review summarizes how Indigenous approaches have been integrated in suicide prevention initiatives targeting Indigenous populations.MethodsA systematic search guided by a community-based participatory research (CBPR) approach was conducted in twelve electronic bibliographic databases for academic literature and six databases for grey literature to identify relevant articles. the reference lists of articles that were selected via the search strategy were hand-searched in order to include any further articles that may have been missed. Articles were screened and assessed for eligibility. From eligible articles, data including authors, year of publication, type of publication, objectives of the study, country, target population, type of suicide prevention strategy, description of suicide prevention strategy, and main outcomes of the study were extracted. A thematic analysis approach guided by Métis knowledge and practices was also applied to synthesize and summarize the findings.ResultsFifty-six academic articles and 16 articles from the grey literature were examined. Four overarching and intersecting thematic areas emerged out of analysis of the academic and grey literature: (1) engaging culture and strengthening connectedness; (2) integrating Indigenous knowledge; (3) Indigenous self-determination; and (4) employing decolonial approaches. ConclusionsFindings demonstrate how centering Indigenous knowledge and approaches within suicide prevention positively contribute to suicide-related outcomes. Initiatives built upon comprehensive community engagement processes and which incorporate Indigenous culture, knowledge, and decolonizing methods have been shown to have substantial impact on suicide-related outcomes at the individual- and community-level. Indigenous approaches to suicide prevention are diverse, drawing on local culture, knowledge, need and priorities.

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)0.052
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.052
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.145
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0330.023
Science and technology studies0.0030.004
Scholarly communication0.0090.009
Open science0.0030.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.495
GPT teacher head0.685
Teacher spread0.189 · 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 designSystematic review
Domainnot available
GenreReview

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".

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Citations2
Published2022
Admission routes2
Has abstractyes

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