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Record W3013224904 · doi:10.1027/0227-5910/a000667

Suicide Prevention in the Americas

2020· article· en· W3013224904 on OpenAlexaffabout
Morton M. Silverman, Loraine Barnaby, Brian L. Mishara, Daniel J. Reidenberg

Bibliographic record

VenueCrisis · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGeographyCaribbean regionLatin AmericansPopulationPsychological interventionSocioeconomicsEconomic growthPolitical scienceDemographyMedicineSociologyEconomics

Abstract

fetched live from OpenAlex

The Americas encompass the entirety of the continents of North America and South America, representing 49 countries. Together, they make up most of Earth's western hemisphere. The population is over 1 billion (2006 figure), with over 65 % living in one of the three most populated countries (the United States, Brazil, and Mexico). The Americas have low-, middle-, and high-income countries. Data from this region have not been readily and consistently available. There are several English-speaking Caribbean nations and countries in South America that have not had updated information. This chapter will focus on suicide prevention within North America (United States and Canada), some countries in the Caribbean region, and some countries in South America. Guyana, Suriname, and Trinidad and Tobago have severe issues with pesticide suicide, with average rates of 44.2 (global rank 1); 27.8 (global rank 5) and 13.0 (global rank 41) per 100,000 respectively. Jamaica, however, had one of the lowest rates: 1.2 per 100,000 (global rank 166). General, regional, and country-specific prevention proposals are suggested, highlighting intersectoral, private collaboration, attention to at-risk persons, substance abuse and mental health interventions, training, and reducing access to lethal means.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.093
GPT teacher head0.382
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2020
Admission routes2
Has abstractyes

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