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Initiating and integrating a personalized end of life care project in a community hospital intensive care unit: a mixed-method study of clinician and key stakeholder perspectives

2020· preprint· en· W3092149833 on OpenAlexafffundabout
Eugenia Yeung, Laurie Sadowski, Kelsea Levesque, Mercedes Camargo, Allen Vo, Elayn Young, Erick Duan, Jennifer Tsang, Benjamin Tam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsImpactMcMaster UniversityNiagara Health SystemUniversity of Ottawa
FundersPhysicians' Services Incorporated Foundation
KeywordsStakeholderEnd-of-life careDyadMedicineNursingIntensive care unitUnit (ring theory)Qualitative propertyQualitative researchData collectionMedical educationPalliative carePsychologyPublic relationsPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Rationale The end of life (EOL) experience in the intensive care unit (ICU) can be psychologically distressing. The 3 Wishes Project (3WP) personalizes the EOL experience by carrying out wishes for dying patients and their families. While the 3WP has been integrated in academic, tertiary care ICUs, implementing this project in a community ICU has yet to be described. Objectives To examine facilitators of, and barriers to, implementing the 3WP in a community ICU from the clinician and key-stakeholder perspective. Methods This mixed-method study evaluated the implementation of the 3WP in a 20-bed community ICU in Southern Ontario, Canada. Patients were considered for the 3WP if they had a high likelihood of imminent death or planned withdrawal of life-sustaining therapy. Quantitative data include patient demographic data and wishes implemented. Following the qualitative descriptive approach, semi-structured interviews were conducted with purposively sampled clinicians and key-stakeholders. Data from transcribed interviews were analyzed in triplicate through qualitative content analysis. Results During the 10-month period, 66 of 67 wishes were completed, with a median of 4.5 wishes per patient-family dyad. Interviews with 12 participants indicated that the 3WP personalized and enriched the EOL experience for patients, families and clinicians. Interviewees indicated higher intensity education strategies were needed to enable spread as the project grew. Clinicians described many physical resources for the project but required more non-clinical project support for orientation, continuing education and data collection. Instead, these roles were completed by clinicians with saturated work capacity which may have inhibited the spread of the project. Conclusions In this community hospital, ICU clinicians and key stakeholders reported the 3WP improved EOL care for patients, families, and clinicians. Project implementation in a community ICU requires investigators take into account project characteristics and adapt the intervention to the community hospital context.

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)0.034
metaresearch head score (Gemma)0.031
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 score0.034
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.005
Scholarly communication0.0060.003
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.300
GPT teacher head0.477
Teacher spread0.177 · 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".

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Citations0
Published2020
Admission routes3
Has abstractyes

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