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An integrated program to train local health care providers to meet post-disaster mental health needs

2005· article· en· W2124769419 on OpenAlexafffundabout
Stan Kutcher, Sonia Chehil, Thorne Roberts

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2005
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsDalhousie University
FundersPan American Health OrganizationDalhousie University
KeywordsMental healthPsychosocialNatural disasterHealth carePopulationIdentification (biology)TrainerNursingMedicineEnvironmental healthPsychiatryPolitical scienceGeographyComputer science

Abstract

fetched live from OpenAlex

This paper describes a post-disaster mental health training program developed by the International Section of the Department of Psychiatry at Dalhousie University (Halifax, Canada) and delivered in Grenada after Hurricane Ivan struck the country in September 2004. This train-the-trainer program used an integrated community health model to help local health care providers develop the necessary skills for the identification and evidenced-based treatment of mental disorders occurring after a natural disaster. The approach also provided for ongoing, sustainable mental health care delivered in the community setting, as advocated by the World Health Organization and the Pan American Health Organization. This approach is in contrast to the largely ineffective and costly vertical whole-population psychosocial counseling activities that have often been used in the Caribbean following natural disasters.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.024
GPT teacher head0.420
Teacher spread0.395 · 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

Citations14
Published2005
Admission routes3
Has abstractyes

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