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Record W68536549 · doi:10.1177/082585970702300203

Psychometric Properties of a Modified Version of the Caregiver Reaction Assessment Scale Measuring Caregiving and Post-Caregiving Reactions of Caregivers of Cancer Patients

2007· article· en· W68536549 on OpenAlexaff
Yaacov G. Bachner, Norm O’Rourke, Sara Carmel

Bibliographic record

VenueJournal of Palliative Care · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsConfirmatory factor analysisPsychologyScale (ratio)Clinical psychologyFamily caregiversGerontologyCaregiver burdenStructural equation modelingMedicineDementia

Abstract

fetched live from OpenAlex

Most care received by cancer patients is provided in the community by informal or unpaid caregivers. The unrelenting care demands can lead to physical, emotional, social, and financial reactions; furthermore, studies indicate that the effects of caregiving may endure after the patient's death. A need therefore exists for instruments measuring both caregiving and post-caregiving reactions. Among available instruments, the Caregiver Reaction Assessment (CRA) is a multidimensional, 5-factor measure designed to assess the negative and positive aspects of caregiving. The current study examined the psychometric properties and factor structure of responses to a modified Hebrew version of the CRA aimed at measuring caregiving and post-caregiving reactions. Although the scale was modified, it was assumed that, similar to the original CRA, a 5-factor structure would be supported by means of confirmatory factor analysis. A total of 236 bereaved primary caregivers of cancer patients from central and southern regions of Israel were recruited over a period of 18 months. As hypothesized, results provide support for a 5-factor structure of responses to this modified version of the CRA. The concurrent validity of responses to the scale was also supported. Replication of the findings with randomly derived and larger sample sizes is needed.

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 score0.137
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.040
GPT teacher head0.318
Teacher spread0.278 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

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