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Record W4404162843 · doi:10.5737/23688076344523

Exploring the Experiences of Cancer Survivors and Their Caregivers Accessing Supportive Care Services in New Brunswick, Canada

2024· article· en· W4404162843 on OpenAlexaffvenueabout
Charlotte Schwarz, Alison Luke, Julia Besner, Luke MacNeill, Lauren Renée Ashfield, Julie Easley, Stephanie McIntosh-Lawrence, Shelley Doucet

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHorizon Health NetworkUniversity of New Brunswick
Fundersnot available
KeywordsDistressNursingMedicineQuality of life (healthcare)Social supportPopulationPsychologyFamily medicineClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Supportive care services can reduce distress and improve quality of life for cancer survivors and their caregivers. However, people often struggle to access these services. With this issue in mind, the current study aimed to explore the experiences of cancer survivors and their caregivers in accessing supportive care services in New Brunswick, Canada, as well as their prospective interest in a provincial supportive care centre. Forty-four individuals participated in an online or mail survey designed to identify experiences accessing supportive care services and supportive care needs. Results indicated the supportive care services that are most important to participants (e.g., mental and emotional support). Many participants noted that they were unaware of the availability of follow-up services and methods of access. Participants had a variety of unmet care requirements including lack of informational support and care coordination. All participants reported that they would like to have a supportive care centre in New Brunswick. These findings offer important recommendations for improving the coordination and delivery of supportive cancer care for this population.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.199
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.101
GPT teacher head0.394
Teacher spread0.293 · 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 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

Citations3
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Oncology Nursing JournalSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207