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Record W2010805742 · doi:10.1177/1359105304040890

‘We Will Never Ever Forget...’: The Swissair Flight 111 Disaster and its Impact on Volunteers and Communities

2004· article· en· W2010805742 on OpenAlexaff
Terry Mitchell, Kara Griffin, Sherry H. Stewart, Pamela Loba

Bibliographic record

VenueJournal of Health Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsVolunteerFocus groupCommunity engagementSuicide preventionPsychological resilienceCommunity resilienceOccupational safety and healthQualitative researchPoison controlEmergency managementHuman factors and ergonomicsDuration (music)PsychologyMedicineEnvironmental healthPublic relationsSocial psychologyPolitical scienceSociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

In collaboration with a Community Advisory Group we examined the impact of the 1998 Swissair Flight 111 disaster on volunteer responders and surrounding communities. We conducted qualitative interviews and administered a set of structured questionnaires to 13 volunteer disaster workers and conducted a focus group with community leaders. Community silence and limited help-seeking behaviour were typical reactions to the SA 111 disaster. The nature and duration of the disaster response efforts contributed to a probable 46 per cent PTSD rate in the community volunteers. Community-based, culturally appropriate followup, as well as the development of volunteer protocols for future disaster response efforts, are necessary to minimize long-term health impacts and to promote resilience among community residents and volunteers exposed to a major disaster.

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)0.001
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 score0.763
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.478
Teacher spread0.378 · 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

Citations42
Published2004
Admission routes1
Has abstractyes

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