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Record W4394727237 · doi:10.7759/cureus.58061

Practicing Surgeons’ Perception of Barriers to Palliative Care Delivery in British Columbia

2024· article· en· W4394727237 on OpenAlexaffabout
Kadhim Taqi, Christina W. Lee, Jenny W Zhang, Philippa Hawley, Rona Cheifetz

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of British ColumbiaCanadian Association of Nurses in Oncology
Fundersnot available
KeywordsPalliative careThematic analysisNursingMedicineWorkflowPerceptionQualitative researchFamily medicinePsychologySociologyManagement

Abstract

fetched live from OpenAlex

BACKGROUND: Utilization of palliative care remains low among surgical patients. We aim to characterize general surgeons' perceptions of barriers to access palliative care in British Columbia (BC). METHODS: Semi-structured interviews were carried out with a total of 11 surgeons in BC. Interviews were transcribed for thematic analysis via interpretive description. Dominant themes were identified and agreed upon between the authors. RESULTS: Several barriers were identified, which include system and institution, communication and surgical workflow barriers. At the system and institutional level, there were difficulties accessing patient information and continuity of care. Themes in the communication included patient misconceptions about palliative care and communication challenges with consulting services. Surgical workflow barriers influenced the overall perceived role of surgeons when caring for patients with palliative care needs. CONCLUSION: Understanding surgeons' perspectives on barriers to palliative care is an important step in changing management. This can aid in the development of strategies that ease access to palliative care.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.121
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.390
Teacher spread0.323 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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