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Record W2270094944

Barriers and facilitators to providing palliative care in rural communities: A nursing perspective.

2012· article· en· W2270094944 on OpenAlexaff
Sharon Kaasalainen, Kevin Brazil, Allison Williams, Donna M. Wilson, Kathleen Willison, Deborah A. Marshall, Alan Taniguchi

Bibliographic record

VenueResearch Portal (Queen's University Belfast) · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPalliative careNursingPerspective (graphical)Qualitative researchMedicineQuality (philosophy)Sociology
DOInot available

Abstract

fetched live from OpenAlex

Nursing plays a key role in the coordination and delivery of palliative care services in rural settings. The purpose of this study is to identify barriers and enablers to providing palliative care in rural communities from a nursing perspective. This study utilized a qualitative descriptive design. Findings highlighted that the remoteness, limited access to resources and professional practice barriers created challenges for nurses as they tried to provide quality palliative care to their clients. System-related barriers were identified and included: lack of services, funding issues, and poor continuity of care. Despite these barriers, nurses drew from supports to optimize palliative care such as using a team approach to care, centers, utilizing local case managers and informal community members, and using palliative care resources. These results may help inform policy decisions around the needs of nurses who practice in rural settings to provide quality care to individuals who are dying and their families.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
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 head0.083
GPT teacher head0.412
Teacher spread0.329 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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