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Record W4403911110 · doi:10.12927/hcq.2024.27433

9-8-8: Suicide Crisis Helpline – Implementing a Pan-Canadian Program to Prevent Suicide

2024· article· en· W4403911110 on OpenAlexaffvenueabout
Allison Crawford, Jenny Hardy, Anne Kirvan, Chantalle Clarkin, Helen D. Davies, Amanda Gambin, Lee Fairclough, Eva Serhal

Bibliographic record

VenueHealthcare Quarterly · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsCARE CanadaCoalition for Research in Women's HealthCentre for Addiction and Mental Health
Fundersnot available
KeywordsHelplineBest practiceSuicide preventionMedical emergencyCrisis interventionSuicide methodsOccupational safety and healthMedicinePoison controlPolitical scienceSuicide ratesPsychiatryEmergency medicineLaw

Abstract

fetched live from OpenAlex

The 9-8-8: Suicide Crisis Helpline was launched in Canada in November 2023, aligned with an international movement to ensure access to crisis supports as part of a public health approach to suicide prevention. We describe the planning and implementation of 9-8-8 in Canada using the RE-AIM framework, considering the Reach, Effectiveness, Adoption, Implementation and Maintenance of 9-8-8 within the first six months of service (Glasgow et al. 2019). There is evidence of reach and adoption of 9-8-8 across Canada, and we discuss evidence-based strategies to evaluate and enhance effectiveness, implementation and maintenance. We also consider the importance of evaluating 9-8-8 within the larger socio-ecological and health system contexts. 9-8-8 must cultivate a learning health system approach and be part of a larger learning health system focused on reducing suicide in Canada.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.420

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.001
Scholarly communication0.0020.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.074
GPT teacher head0.497
Teacher spread0.422 · 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 designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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