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Record W4383823257 · doi:10.1037/rep0000498

Subjective well-being of adults with multiple sclerosis during COVID-19: Evaluating stress–appraisal–coping and person–environment factors.

2023· article· en· W4383823257 on OpenAlexaff
Kanako Iwanaga, Fong Chan, Phillip D. Rumrill, Nicole Ditchman

Bibliographic record

VenueRehabilitation Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsEducation and Early Childhood Development
FundersVirginia Commonwealth University
KeywordsPsychologyCoping (psychology)Cognitive appraisalMultilevel modelPsycINFOClinical psychologyPsychological interventionBivariate analysisMental healthStress managementSocial supportWell-beingDevelopmental psychologySocial psychologyPsychiatryMEDLINEPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: People with multiple sclerosis (MS) have been coping with high levels of stress during the ongoing coronavirus pandemic, affecting their employment, physical, and mental health, and overall life satisfaction. OBJECTIVE: This study evaluated constructs of the stress-appraisal-coping theory and positive person-environment factors as predictors of subjective well-being for adults with MS. METHOD: Participants included 477 adults with MS recruited through the National Multiple Sclerosis Society. Hierarchical regression analysis was used to determine the incremental variance in subjective well-being accounted for by demographic covariates, functional disability, perceived stress, stress appraisal, coping styles, and positive person-environment contextual factors. RESULTS: ² = 1.48; large effect size). CONCLUSIONS: Findings from this study support a stress management and well-being model based on constructs of Lazarus and Folkman's stress-appraisal-coping theory and positive person-environment contextual factors, which can inform the development of theory-driven and empirically supported stress management and well-being interventions for people with MS during the ongoing global health crisis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.001
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 score0.070
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.075
GPT teacher head0.417
Teacher spread0.342 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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