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Ocean waves and roadside spirits: Thai health service providers' post‐tsunami psychosocial health

2012· review· en· W2037553026 on OpenAlexafffund
Emma Varley, Wanrudee Isaranuwatchai, Peter C. Coyte

Bibliographic record

VenueDisasters · 2012
Typereview
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsPsychosocialPsychological interventionService providerOccupational safety and healthSuicide preventionSituatedMental healthPoison controlMedicineNursingService (business)Environmental healthBusinessPsychiatry

Abstract

fetched live from OpenAlex

A massive earthquake off the west coast of Sumatra in Indonesia triggered a tsunami on 26 December 2004. At least five million people around the world were affected, and the total number of deaths exceeded 280,000. In Thailand, the tsunami struck six southern provinces, where the disaster's immediate impact was catastrophic. Based on ethnographic fieldwork in Phang Nga Province (2007), this paper provides an overview of the disaster's psychosocial consequences for Thai health service providers, the vast majority of whom were bypassed by regional post-tsunami mental health initiatives. The available tsunami literature only briefly attends to health providers' experience of professional 'burn-out', rather than explores the tsunami's wide spectrum of psychosocial effects. This research aims to remedy such oversights through 'critical medical' and 'interpretive phenomenological' analysis of the diverse and culturally-situated ways in which health providers' experienced the tsunami. The paper concludes by arguing for disaster-related psychosocial interventions to involve health providers explicitly.

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)0.004
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.127
GPT teacher head0.469
Teacher spread0.343 · 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
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".

Quick stats

Citations12
Published2012
Admission routes2
Has abstractyes

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