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Record W2145489907 · doi:10.1017/s1049023x00003605

In the Path of Disasters: Psychosocial Issues for Preparedness, Response, and Recovery

2006· review· en· W2145489907 on OpenAlexaff
Carol Amaratunga, Tracey O’Sullivan

Bibliographic record

VenuePrehospital and Disaster Medicine · 2006
Typereview
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
FundersCenters for Disease Control and Prevention
KeywordsPsychosocialEmergency managementPreparednessPsychologyMedicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

The psychosocial impacts of disasters are profound. In recent years, there have been too many reminders of these impacts and the dire needs of the people involved. The purpose of this article is to present the following themes from the psychosocial literature on disasters and emergency management: (1) differential impacts of disasters according to gender and age; (2) prevention efforts to reduce racial discrimination, rape, and other forms of abuse; (3) readiness for cultural change toward prevention and preparedness; and (4) the need to involve aid beneficiaries as active partners in relief strategies, particularly during reconstruction of communities and critical systems. Psychosocial needs change throughout the disaster cycle, particularly as social support deteriorates over time. It is important to anticipate what psychosocial needs of the public, emergency responders, support staff, and volunteers might emerge, before advancing to the next stage of the disaster. Particular consideration needs to be directed toward differential impacts of disasters based on gender, age, and other vulnerabilities.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.383
Teacher spread0.348 · 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
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

Citations27
Published2006
Admission routes1
Has abstractyes

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