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Record W4409263894 · doi:10.1186/s12889-025-22465-1

Developing self-efficacy and ‘communities of practice’ between community and institutional partners to prevent suicide and increase mental health in under-resourced communities: expanding the research constructs for upstream prevention

2025· article· en· W4409263894 on OpenAlexaff
Lisa Wexler, Lauren White, Joel Ginn, Tara Schmidt, Suzanne Rataj, Caroline C. Wells, Katie Schultz, Eleni A. Kapoulea, Diane McEachern, Patrick Habecker, Holly Laws

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
FundersNational Institute of Mental Health
KeywordsBiostatisticsPublic healthMental healthMedicineUpstream (networking)Suicide preventionCommunity healthImplementation researchEpidemiologyEnvironmental healthPoison controlNursingPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Suicide is a serious and growing health inequity for Alaska Native (AN) youth (ages 15–24), who experience suicide rates significantly higher than the general U.S. youth population. In under-served, remote AN communities, building on existing local and cultural resources can increase uptake of prevention behaviors like lethal means reduction, interpersonal support, and postvention by family members, workers and community members, which can be important for preventing suicide in places where mental health services are sparce. This study expands the variables we hypothesize as important for reducing suicide risk and supporting mental wellness. These variables are: (1) perceived suicide prevention self-efficacy, (2) perceived wellness self-efficacy, and (3) developing a ‘community of practice’ (CoP) for prevention/wellness work. METHOD: With a convenience sample (N = 398) of participants (ages 15+) in five remote AN communities, this study characterizes respondents’ social roles: institutional role if they have a job that includes suicide prevention (e.g. teachers, community health workers) and community role if their primary role is based on family or community positioning (e.g. Elder, parent). The cross-sectional analysis then explores the relationship between respondents’ wellness and prevention self-efficacy and CoP as predictors of their self-reported suicide prevention and wellness promotion behaviors: (1) working together with others (e.g. community initiatives), (2) offering interpersonal support to someone (3), reducing access to lethal means, and (4) reducing suicide risk for others after a suicide death in the community. RESULTS: Community and institutional roles are vital, and analyses detected distinct patterns linking our dependent variables to different preventative behaviors. Findings associated wellness self-efficacy and CoP (but not prevention self-efficacy) with “working together” behaviors, wellness and prevention self-efficacy (but not CoP) with interpersonal supportive behaviors; both prevention self-efficacy and CoP with higher postvention behaviors. Only prevention self-efficacy was associated with lethal means reduction. CONCLUSIONS: The study widens the scope of suicide prevention. Promising approaches to suicide prevention in rural low-resourced communities include: (1) engaging people in community and institutional roles (2), developing communities of practice for suicide prevention among different sectors of a community, and (3) broadening the scope of suicide prevention to include wellness promotion as well as suicide prevention.

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)0.008
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.302
GPT teacher head0.510
Teacher spread0.207 · 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
Published2025
Admission routes1
Has abstractyes

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