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Record W2017360937 · doi:10.1080/13607860802154473

Mortality communication as a predictor of psychological distress among family caregivers of home hospice and hospital inpatients with terminal cancer

2009· article· en· W2017360937 on OpenAlexaff
Yaacov G. Bachner, Norm O’Rourke, Eldad Davidov, Sara Carmel

Bibliographic record

VenueAging & Mental Health · 2009
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPsychological distressDistressMedicineTerminal cancerClinical psychologyFamily caregiversPath analysis (statistics)Depressive symptomsAssociation (psychology)PsychologyPsychiatryCancerGerontologyMental healthInternal medicinePsychotherapistAnxiety

Abstract

fetched live from OpenAlex

Terminally ill cancer patients and their caregivers experience significant difficulties discussing illness and impending death (herein defined as mortality communication). The current study compares response levels as well as patterns of association between mortality communication and psychological distress among caregivers of home hospice and hospital inpatients. For this study, 231 family caregivers were recruited within a year of bereavement from the south and central health regions of Israel. Contrary to our initial hypothesis, retrospectively reported levels of mortality communication did not differ between groups; however, lower levels of depressive symptomatology were reported by home hospice caregivers. Separate path analytic models indicate statistically significant inverse associations between mortality communication and psychological distress (i.e. depressive symptomatology, emotional exhaustion). Invariance analyses indicate that the strength of association between variables did not differ between path models. The results of this study are discussed in terms of self-selection biases and possible confounds associated with retrospective reporting among bereaved caregivers.

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)0.000
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 score0.026
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.374
Teacher spread0.351 · 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

Citations27
Published2009
Admission routes1
Has abstractyes

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