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Record W1971680118 · doi:10.1177/1043659609357634

Cancer Care Experiences and the Use of Complementary and Alternative Medicine at End of Life in Nova Scotia’s Black Communities

2010· article· en· W1971680118 on OpenAlexafffundabout
Victor Maddalena, Wanda Thomas Bernard, Josephine Etowa, Sharon Davis Murdoch, Donna L. Smith, Phyllis Marsh Jarvis

Bibliographic record

VenueJournal of Transcultural Nursing · 2010
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCancer Care Nova ScotiaNova Scotia Health AuthorityDalhousie UniversityMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsNova scotiaPalliative careEnd-of-life careSpiritualityMedicineFocus groupGerontologyQualitative researchFamily medicinePreferenceNursingAlternative medicineSociology

Abstract

fetched live from OpenAlex

PURPOSE: This qualitative study examines the meanings that African Canadians living in Nova Scotia, Canada, ascribe to their experiences with cancer, family caregiving, and their use of complementary and alternative medicine (CAM) at end of life. DESIGN: Case study methodology using in-depth interviews were used to examine the experiences of caregivers of decedents who died from cancer in three families. FINDINGS: For many African Canadians end of life is characterized by care provided by family and friends in the home setting, community involvement, a focus on spirituality, and an avoidance of institutionalized health services. Caregivers and their families experience multiple challenges (and multiple demands). There is evidence to suggest that the use of CAM and home remedies at end of life are common. DISCUSSION: The delivery of palliative care to African Canadian families should consider and support their preference to provide end-of-life care in the home setting.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.075
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.005
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.206
GPT teacher head0.432
Teacher spread0.226 · 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

Citations24
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Transcultural NursingSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207