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Record W2766046647 · doi:10.1007/s10597-020-00640-5

Factors Associated with Distress in Caregivers of People with Personality Disorders

2020· article· en· W2766046647 on OpenAlexaff
Paige Lamborn, Kenneth M. Cramer

Bibliographic record

VenueCommunity Mental Health Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStressorWorryPsychologyDistressClinical psychologyPersonalityInterpersonal communicationSocial supportPsychological distressInterpersonal relationshipMental healthAnxietyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The present study investigated how stressors experienced by caregivers of people with personality disorders relate to each other and psychological distress, using the Stress Process Model (Pearlin et al. in Gerontologist 30(5):583-594, https://doi.org/10.1093/geront/30.5.583 , 1990). A community sample of caregivers (N = 106) completed an online survey. Partial Least Squares Path Modelling revealed that caregivers who were male, younger, or residing with their loved one were more likely to experience stressors. Salient primary stressors included the caregivers' worry and care-receivers' levels of instrumental demands and interpersonal problems. Important secondary stressors included strains in the caregivers' schedules, family relationships, and health, as well as reduced mastery and caregiving esteem. The model provided preliminary support for a pathway from demographic and relationship characteristics, through primary and secondary stressors, to heightened psychological distress. The present study clarifies the way caregiving stressors give rise to psychological distress; directions for future research are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.339
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2020
Admission routes1
Has abstractno

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