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Record W2601919031 · doi:10.1177/1054773817699931

Psychometric Testing of Two Chinese-Version Scales on Attitudes Toward and Caregiving Behaviors for End-of-Life Patients and Families

2017· article· en· W2601919031 on OpenAlexfundno aff
Luke Yang, Yung-Fang Liu, Huey-Fang Sun, Hsien-Hsien Chiang, Yu‐Lun Tsai, Jen‐Jiuan Liaw

Bibliographic record

VenueClinical Nursing Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersTri-Service General HospitalFamily Process InstituteRegistered Nurses' Association of OntarioNational Defense Medical Center
KeywordsScale (ratio)Cronbach's alphaPsychologyGriefClinical psychologyTest (biology)PsychometricsPsychiatry

Abstract

fetched live from OpenAlex

The study purpose was to examine the validities and reliabilities of the Chinese-versions Frommelt Attitudes Toward Care of the Dying Scale (Attitudes Scale) and Caregiving Behaviors Scale for End-of-Life Patients and Families (Behaviors Scale). The scales were tested in a convenience sample of 318 nurses with ≥6 months work experience at three hospitals. Cronbach's alphas of the Attitudes and Behaviors Scales were .90 and .96, respectively. Each scale had Kaiser-Meyer-Olkin index >.85 and Bartlett's test of sphericity >4000 ( p < .001). Attitudes Scale loaded on three factors: respecting and caring for dying patients and families, avoiding care of the dying, and involving patients and families in end-of-life care. The Behaviors Scale loaded on two factors: supporting dying patients and families, and helping families cope with grief. Factor loadings for both scales were ≥.49. Both Attitudes and Behaviors Scales are reliable and valid for evaluating nurses' attitudes and caregiving behaviors for the dying.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.435
GPT teacher head0.606
Teacher spread0.172 · 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 designBench or experimental
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

Citations5
Published2017
Admission routes1
Has abstractyes

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