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Record W77700991 · doi:10.1177/082585971102700209

Hospice Residents’ Interest in Complementary and Alternative Medicine (Cam) at end of Life: A Pilot Study in Hospice Residences in British Columbia

2011· article· en· W77700991 on OpenAlexafffundabout
Sherin Rahim-Jamal, Ann F. Sarte, Jean Kozak, Kathy Bodell, Maria Cristina Barroetavena, Romayne Gallagher, Anne Leis

Bibliographic record

VenueJournal of Palliative Care · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of SaskatchewanFraser HealthUniversity of British ColumbiaProvidence Health Care
FundersBC Cancer AgencyLotte and John Hecht Memorial Foundation
KeywordsFamily medicineQuality of life (healthcare)MedicineOpenness to experienceEnd-of-life carePalliative careHospice careFamily memberGerontologyNursingPsychology

Abstract

fetched live from OpenAlex

While complementary and alternative medicine (CAM) can improve quality of life at end of life, little research exists on hospice residents' interest in using and sharing CAM experiences with a partner/friend/other family member. A pilot study conducted in British Columbia, Canada explored the extent of hospice residents' interest and openness to CAM use. A convenience sample of 48 hospice residents from 9 hospice sites completed questionnaire-based interviews. The majority of participants were Caucasian women over 60 years old. 81 percent expressed interest in receiving CAM; 79 percent used CAM prior to entering the hospice setting. 50 percent of those interested in using CAM felt their partner/friend/other family member would also be interested in receiving CAM, and half of that 50 percent reported personal interest in sharing the experience. Reasons reported for CAM interest were to enhance well-being, relaxation, and for pain relief. Further research could explore how resident-caregiver dyads may benefit from shared CAM experiences over the illness trajectory.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.540
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.278
GPT teacher head0.417
Teacher spread0.138 · 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

Citations4
Published2011
Admission routes3
Has abstractyes

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