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Record W2102813926 · doi:10.1177/1084822310384920

Exploring Family Caregivers’ Rationales for Nonuse of Formal Home Health Services When Caring for a Dying Family Member

2010· article· en· W2102813926 on OpenAlexafffundabout
Laura Funk, Kelli Stajduhar, Denise Cloutier

Bibliographic record

VenueHome Health Care Management & Practice · 2010
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Victoria
FundersMichael Smith Health Research BC
KeywordsNursingCompetence (human resources)Coping (psychology)Home healthPerceptionFamily memberPsychologyHealth careMedicineFamily medicineSocial psychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Drawing on interviews with 26 bereaved individuals who provided care to a dying family member at home, the purpose of this article is to identify and explore motivations behind the “nonuse” of home health services. Participants resided in a Western Canadian regional health authority. In their accounts of not asking for help from formal providers, participants highlighted personal reasons, coping needs, and a lack of knowledge. In their accounts of refused help, they placed more emphasis on a perceived lack of need for help (including constructing themselves as capable and coping), as well as their perceptions of the quality and competence of formal services. Findings are compared against existing literature. Implications for health system policy and planning, and for home health nursing practice, training and education, particularly with reference to situations in which responsibility is left with families to ask for help from formal providers, are also 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)0.019
metaresearch head score (Gemma)0.050
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.011
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.005
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 head0.195
GPT teacher head0.425
Teacher spread0.230 · 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 designQualitative
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

Citations8
Published2010
Admission routes3
Has abstractyes

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