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Record W2769595893 · doi:10.5539/gjhs.v10n1p76

Men’s Help-Seeking and Health in Natural Disaster Contexts

2017· article· en· W2769595893 on OpenAlexaffvenue
Óscar Labra, Gilles Tremblay, Agustin Ependa, Gabriel Gingras Lacroix

Bibliographic record

VenueGlobal Journal of Health Science · 2017
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsPsychosocialNatural disasterMasculinityContext (archaeology)PsychologyNatural (archaeology)Qualitative researchEvent (particle physics)Hegemonic masculinitySocial psychologySociologyGeographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The study examines masculinity practices, in both their positive and negative aspects, in terms of their influence on men’s help-seeking in the context of a natural disaster, in particular the rejection of psychosocial assistance.METHODOLOGY: Qualitative study of a small sample of voluntary participants constituted of male survivors of a major earthquake and tsunami event in 2010 in Chile.RESULTS: It appears that norms of hegemonic masculinity predominated in men’s emotional responses to the disaster event, with both positive and negative consequences. Family relationships, mutual assistance in the community, and the passage of time emerge as the principal factors of healing for men since the catastrophe.DISCUSSION: An understanding of men’s beliefs and attitudes is, therefore, essential to any inclusive assessment of the efficacy and quality of the various services offered to populations exposed to natural disaster events.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.467
Teacher spread0.424 · 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 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

Citations6
Published2017
Admission routes2
Has abstractyes

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