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Record W2560517174 · doi:10.14417/ap.1122

Coping as a moderator of the influence of economic stressors on psychological health

2016· article· en· W2560517174 on OpenAlexaff
Saul Neves de Jesús, Ana Rita Cavaco Leal, João Viseu, Patrícia Oom do Valle, Rafaela Dias Matavelli, Joana Pereira, Esther R. Greenglass

Bibliographic record

VenueAnálise Psicológica · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork University
Fundersnot available
KeywordsStressorModerationCoping (psychology)AnxietyMental healthStructural equation modelingPsychologyPsychological healthClinical psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Since 2008, there has been a decline in the economy of several European countries, including Portugal. In the literature, it is emphasized that periods of economic uncertainty propitiate the appearance of mental health problems and diminish populations’ well-being. The aim of the present study, with 729 Portuguese participants, 33.9% (n = 247) males and 66.1% (n = 482) females with an average age of, approximately, 37 years old (M = 36.99; SD = 12.81), was to examine the relationship between economic hardship, financial threat, and financial well-being (i.e., economic stressors) and stress, anxiety, and depression (i.e., psychological health indicators), as well as to test the moderation effect of coping in the aforementioned relationship. To achieve these goals, a cross-sectional design was implemented and structural equation modeling (SEM) was used to analyze the obtained data. The results showed that coping decreased the influence of economic stressors on psychological health indicators, thus protecting individuals’ psychological health from the negative consequences associated with adverse economic situations.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.065
GPT teacher head0.432
Teacher spread0.367 · 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

Citations28
Published2016
Admission routes1
Has abstractyes

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