MétaCan
Menu
Back to cohort
Record W2754741597 · doi:10.1155/2017/3457103

Association of Stressful Life Events with Psychological Problems: A Large-Scale Community-Based Study Using Grouped Outcomes Latent Factor Regression with Latent Predictors

2017· article· en· W2754741597 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputational and Mathematical Methods in Medicine · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Alberta
FundersIsfahan University of Medical Sciences
KeywordsScale (ratio)Association (psychology)RegressionRegression analysisLatent variablePsychologyClinical psychologyStatisticsMathematicsGeography

Abstract

fetched live from OpenAlex

Objective . The current study is aimed at investigating the association between stressful life events and psychological problems in a large sample of Iranian adults. Method . In a cross-sectional large-scale community-based study, 4763 Iranian adults, living in Isfahan, Iran, were investigated. Grouped outcomes latent factor regression on latent predictors was used for modeling the association of psychological problems (depression, anxiety, and psychological distress), measured by Hospital Anxiety and Depression Scale (HADS) and General Health Questionnaire (GHQ-12), as the grouped outcomes, and stressful life events, measured by a self-administered stressful life events (SLEs) questionnaire, as the latent predictors. Results . The results showed that the personal stressors domain has significant positive association with psychological distress (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.19</mml:mn></mml:math>), anxiety (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.25</mml:mn></mml:math>), depression (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.15</mml:mn></mml:math>), and their collective profile score (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.20</mml:mn></mml:math>), with greater associations in females (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.28</mml:mn></mml:math>) than in males (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mn fontstyle="italic">0.13</mml:mn></mml:math>) (all <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mi>P</mml:mi><mml:mo>&lt;</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>). In addition, in the adjusted models, the regression coefficients for the association of social stressors domain and psychological problems profile score were 0.37, 0.35, and 0.46 in total sample, males, and females, respectively (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mi>P</mml:mi><mml:mo>&lt;</mml:mo><mml:mn fontstyle="italic">0.001</mml:mn></mml:math>). Conclusion . Results of our study indicated that different stressors, particularly those socioeconomic related, have an effective impact on psychological problems. It is important to consider the social and cultural background of a population for managing the stressors as an effective approach for preventing and reducing the destructive burden of psychological problems.

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.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.271
GPT teacher head0.546
Teacher spread0.275 · 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