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Record W1528119717 · doi:10.30849/rip/ijp.v38i1.845

Psychological responses to drought in northeastern Brazil

2017· article· en· W1528119717 on OpenAlexaff
Angela Elizabeth Lapa Coêlho, John G. Adair, Jane S.P. Mocellin

Bibliographic record

VenueRevista Interamericana de Psicología/Interamerican Journal of Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyGeography

Abstract

fetched live from OpenAlex

This study of cumulative effects of drought in Northeast Brazil assessed the psychological responses (anxiety, emotional distress, and PTSD) of 102 individuals living in a city (Queimadas) in a drought-prone area compared to the responses of 102 persons living in a drought-free control city (Areia) of comparable size. As predicted, the findings revealed that residents in the drought area (Queimadas) had significantly higher levels of anxiety and emotional distress than residents in the no-drought area (Areia). In the drought area, women had significantly higher levels of anxiety and men had significantly higher levels of emotional distress than women and men, respectively, in the no-drought area. Likely because of their role vulnerability, women had significantly higher levels of anxiety and emotional distress than men. As predicted, Post-traumatic Stress Disorder (PTSD) was unrelated to the drought. Although descriptive, the results provide baseline data for comparisons as the drought deepens and offer insights and suggestions for further research into the psychological consequences of drought.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.081
GPT teacher head0.485
Teacher spread0.404 · 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

Citations76
Published2017
Admission routes1
Has abstractyes

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