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Record W4395001395 · doi:10.1007/s44202-024-00148-z

Sociocultural determinants of psychological distress and coping among South Asian individuals with chronic illness

2024· article· en· W4395001395 on OpenAlexaff
Ahtisham Younas, Hussan Zeb, Ijaz Arif, Aimal Khan, Arshad Ali, Akhtar Ali, Faisal Aziz

Bibliographic record

VenueDiscover Psychology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCoping (psychology)Psychological distressPsychologyDistressSociocultural evolutionClinical psychologyPsychiatryAnxietySociology

Abstract

fetched live from OpenAlex

Abstract Sociocultural determinants affect the physical and mental well-being and coping of individuals with chronic illnesses. However, no studies have examined the relationship of sociocultural determinants with psychological distress and coping. The purpose of this study was to determine the levels of psychological distress and coping among individuals with chronic illness and identify sociocultural determinants affecting distress and coping. A cross-sectional design was used. Data were collected from a convenience sample of 384 individuals admitted to inpatient settings and attending outpatient clinics at two tertiary care hospitals. The validated Urdu-translated version of the Hospital Anxiety and Depression Scale, and the Brief COPE scales were used for data collection. The mean HADS-Depression score was (11.38 ± 2.53), and the HADS-Anxiety score was 13.42 ± 2.34), indicating high levels of depression and anxiety. The most commonly used coping strategies were problem-focused coping (15.95 ± 4.95), followed by emotion-focused coping (15.01 ± 2.33), and avoidance coping (13.89 ± 4.77). Higher levels of psychological distress in individuals with chronic illnesses underscore the importance of implementing community-based support approaches. Varied use of coping strategies was influenced by the type of chronic illness, living conditions, educational level, years of living with a chronic illness, family dynamics, and available support systems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.987

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.454
Teacher spread0.411 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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