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Record W2410579026 · doi:10.1177/082585971503100405

An Interhospital, Interdisciplinary Needs Assessment of Palliative Care in a Community Critical Care Context

2015· article· en· W2410579026 on OpenAlexafffund
Aimee Sarti, Frances Fothergill Bourbonnais, Angèle Landriault, Stephanie Sutherland, Pierre Cardinal

Bibliographic record

VenueJournal of Palliative Care · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsPalliative careNursingContext (archaeology)End-of-life careMedicineIntensive care unitHealth careUnit (ring theory)PsychologyIntensive care medicine

Abstract

fetched live from OpenAlex

AIM: There is a paucity of data on the provision of palliative care in the critical care settings of smaller community hospitals. This study aimed to identify the gaps that affect the provision of palliative care in a community critical care setting. SETTING: The study was set in a 10-bed, open intensive care unit and emergency department at a community hospital. METHODS: Mixed methods were used. Quantitative data included those drawn from databases and surveys; qualitative data included those collected from interviews, focus groups, and onsite walk-throughs and were analyzed with inductive coding techniques. RESULTS: Gaps were identified in palliative care, goals of care and end-of-life discussions, and resources. Community hospital healthcare professionals did not fully appreciate their essential contribution to the provision of palliative care in the intensive care unit. In addition, there was a lack of expertise, and a lack of interest in gaining expertise, in palliative/end-of-life care. CONCLUSION: Interrelated needs in a complex interprofessional, interhospital context were captured. Further studies are required to obtain data on palliative practice in the care of critically ill patients in various community hospital contexts.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.174
GPT teacher head0.516
Teacher spread0.342 · 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.

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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

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