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Record W4225316906

The mediating role of illness cognitions in the relationship between caregiving demands and caregivers' psychological adjustment

2022· article· en· W4225316906 on OpenAlexaff
Fatemeh Akbari, Somayyeh Mohammadi, Mohsen Dehghani, Robbert Sanderman, M Hagedoorn

Bibliographic record

VenueResearch Repository (Kingston University London) · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLearned helplessnessFeelingPsychological interventionAffect (linguistics)CognitionDistressAssociation (psychology)Clinical psychologyMedicinePsychiatryPsychologyPsychotherapistSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The present study investigated whether illness cognitions mediated the relationship between caregiving demands and positive and negative indicators of adjustment in partners of patients with chronic pain. The sample of this cross-sectional study consisted of 151 partners (mean age=61.4 y, SD=13.6 y, 57% male) of patients with chronic pain (eg, back pain). The study was conducted in the Pain Centre of the University Medical Centre Groningen, The Netherlands, during November 2014 to June 2015. Participants completed questionnaires that assessed caregiving demands, illness cognitions, perceived burden, distress, positive affect, and life satisfaction. The results showed that among illness cognitions, acceptance of the illness mediated the association between caregiving demands and burden (b=0.16, 95% confidence interval [CI]: 0.05-0.28) and positive affect (b=−0.21, CI: −0.41 to −0.06). Helplessness mediated the association between caregiving demands and burden (b=0.46, CI: 0.26-0.69) and distress (b=0.35, CI: 0.19-0.53). Perceived benefits did not mediate any of these associations. The findings indicate that partners who experience more demands tend to appraise the consequences of the patients’ pain condition more negatively, which in turn is associated with their emotional adjustment.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.038
GPT teacher head0.328
Teacher spread0.290 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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