MétaCan
Menu
Back to cohort
Record W3187052580 · doi:10.1155/2021/4937241

An Exploratory Investigation into the Roles of Critical Care Response Teams in End-of-Life Care

2021· article· en· W3187052580 on OpenAlexaff
Adrienne Kwong, Stéphanie Chenail, Aimee Sarti, Laura H. Thompson, Marlena Dang Nguyen, Kwadwo Kyeremanteng, Michael Hartwick

Bibliographic record

VenueCritical Care Research and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsInstitut du Savoir MontfortOttawa Hospital
Fundersnot available
KeywordsEnd-of-life careNursingMedicineThematic analysisPalliative careMultidisciplinary approachAuditDocumentationQualitative research

Abstract

fetched live from OpenAlex

BACKGROUND: Critical Care Response Teams (CCRTs) represent an important interface between end-of-life care (EOLC) and critical care medicine (CCM). The aim of this study was to explore the roles and interactions of CCRTs in the provision of EOLC from the perspective of CCRT members. METHODS: Twelve registered nurses (RNs) and four respiratory therapists (RTs) took part in focus groups, and one-on-one interviews were conducted with six critical care physicians. Thematic coding using a modified constructivist grounded theory approach was used to identify emerging themes through an iterative process involving a four-member coding team. RESULTS: Three main perspectives were identified that spoke to CCRT interactions and perceptions of EOLC encounters. CCRT members felt that they provide a unique skill set of multidisciplinary expertise in treating critically ill patients and evaluating the utility of intensive care treatments. However, despite feeling that they possessed the skills and resources to deliver quality EOLC, CCRT members were ambivalent with respect to whether EOLC was a part of their mandate. Challenges were also identified that impacted the ability of CCRTs to deliver quality EOLC. CONCLUSIONS: This research aids in understanding for the first time CCRT roles in EOLC from the perspectives of individual CCRT members themselves. While CCRTs provide unique multidisciplinary expertise to evaluate the utility of intensive care treatments, opportunities exist to support CCRTs in EOLC, such as dedicated EOLC training, protocols for advance care planning, documentation, and transitions 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 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)0.002
metaresearch head score (Gemma)0.109
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.109
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.279
GPT teacher head0.553
Teacher spread0.274 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCritical Care Research and PracticeSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207