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Record W4389611674 · doi:10.1097/jnn.0000000000000745

The Relationship Between Care Preparedness and Altruism Levels in Caregivers of Stroke Patients

2023· article· en· W4389611674 on OpenAlexaff
Ayşe Çekici, Afife Yurttaş

Bibliographic record

VenueJournal of Neuroscience Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsAltruism (biology)PreparednessStroke (engine)MedicinePopulationScale (ratio)DonationDescriptive statisticsDescriptive researchPsychologyGerontologySocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

ABSTRACT: BACKGROUND: Stroke care partners and caregivers experience emotional and physical burden, and 80% of stroke patients need support after discharge. This study examines the relationship between caregiver preparedness and altruism levels of stroke patients. METHODS: The population of this descriptive and correlational study consisted of the caregivers of stroke patients who were hospitalized at the stroke center of a hospital between January 2021 and August 2021. The sample was determined as 240 with the known sampling formula. The descriptive information form, the Preparedness for Caregiving Scale, and the Altruism Scale were used to collect the study data. RESULTS: The total mean score of the caregivers' preparedness to provide care was found to be 25.04 (7.36), and the mean total altruism score was 85.78 (9.20). The mean score of Donation, which is one of the subdimensions of the Altruism Scale, was 26.67 (4.08), and that of Helping Status was 59.10 (6.92). No statistically significant relationships were detected between caregivers' preparedness to provide care scores, Helping Status scores, Donation scores, and total altruism scores in this study ( P = .241, P = .245, and P = .129, respectively). CONCLUSION: No statistically significant relationships were detected between the preparedness and altruism levels of the caregivers of the stroke patients in this study.

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)0.002
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 score0.009
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.068
GPT teacher head0.357
Teacher spread0.289 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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