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Record W3026378509 · doi:10.1080/22423982.2020.1766319

Perspectives of Nunavut patients and families on their cancer and end of life care experiences

2020· article· en· W3026378509 on OpenAlexafffundabout
Tracey Galloway, Sidney Horlick, Maria Cherba, Madeleine Cole, Roberta L. Woodgate, Gwen Healey Akearok

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsNOSM UniversityUniversity of ManitobaNunavut Research InstituteUniversity of TorontoQaujigiartiit Health Research CentreLaurentian University
FundersCanadian Institutes of Health Research
KeywordsReferralService providerSummative assessmentNursingHealth careService (business)End-of-life careMedicineCommunity healthConsistency (knowledge bases)Palliative carePsychologyFamily medicinePublic healthFormative assessmentPolitical scienceBusinessPedagogy

Abstract

fetched live from OpenAlex

Model for Community Health Research, we conducted in-depth interviews with 10 patients and family members living in Nunavut communities who experienced cancer or end of life care. Results included the following themes: difficulties associated with extensive medical travel; preference for care within the community and for family involvement in care; challenges with communication; challenges with culturally appropriate care; and the value of service providers with strong ties to the community. These themes emphasise the importance of health service capacity building in Nunavut with emphasis on Inuit language and cultural knowledge. They also underscore efforts to improve the quality and consistency of communication among health service providers working in both community and southern referral settings and between service providers and the patients and families they serve.

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)0.001
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 score0.039
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.031
GPT teacher head0.352
Teacher spread0.320 · 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

Citations22
Published2020
Admission routes3
Has abstractyes

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