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Record W2046280496 · doi:10.1017/s1478951504040350

Giving support and getting help: Informal caregivers' experiences with palliative care services

2004· article· en· W2046280496 on OpenAlexaffabout
Roy Cain, Michael MacLean, Scott Sellick

Bibliographic record

VenuePalliative & Supportive Care · 2004
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsThunder Bay Regional Health Sciences CentreUniversity of ReginaMcMaster University
Fundersnot available
KeywordsPalliative careFocus groupNursingMedicineService (business)Terminally illEnd-of-life careHuman immunodeficiency virus (HIV)PsychologyFamily medicineGerontologySociologyBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: Palliative care services have made significant contributions to those needing end-of-life care, but the effect of these services on informal caregivers is less clear. This article reviews the literature and examines the influences of palliative care services on caregivers of people who are dying of cancer, HIV-related illnesses, and illnesses of later life. METHODS: Based on questions that we developed from the literature review, we conducted six focus groups in Toronto, Thunder Bay, and Ottawa, Canada, with informal caregivers about their experiences with caregiving and with palliative care services. RESULTS: We outline the major themes relating to the 42 focus group participants' experiences of giving support and getting help. SIGNIFICANCE OF RESULTS: Our findings help us better understand the common concerns of caregivers of terminally ill seniors, people with HIV/AIDS, and people with cancer. The article discusses the implications of participants' experiences for palliative care service providers.

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)0.008
metaresearch head score (Gemma)0.023
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 score0.020
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.009
Scholarly communication0.0040.004
Open science0.0010.008
Research integrity0.0020.003
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 head0.037
GPT teacher head0.352
Teacher spread0.315 · 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

Citations52
Published2004
Admission routes2
Has abstractyes

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