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Record W1934711913 · doi:10.7326/m15-0502

Personalizing Death in the Intensive Care Unit: The 3 Wishes Project

2015· article· en· W1934711913 on OpenAlexaffabout
Marilyn Swinton, Feli Toledo, France Clarke, Trudy Rose, Tracey Hand-Breckenridge, Anne Boyle, Anne Woods, Nicole Zytaruk, Diane Heels‐Ansdell, Robert Sheppard

Bibliographic record

VenueAnnals of Internal Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineEnd-of-life careIntensive care unitDehumanizationHonorHealth careQuality of life (healthcare)Palliative careIntensive careMEDLINENursingQualitative researchFamily memberFamily medicinePsychiatryIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Dying in the complex, efficiency-driven environment of the intensive care unit can be dehumanizing for the patient and have profound, long-lasting consequences for all persons attendant to that death. OBJECTIVE: To bring peace to the final days of a patient's life and to ease the grieving process. DESIGN: Mixed-methods study. SETTING: 21-bed medical-surgical intensive care unit. PARTICIPANTS: Dying patients and their families and clinicians. INTERVENTION: To honor each patient, a set of wishes was generated by patients, family members, or clinicians. The wishes were implemented before or after death by patients, families, clinicians (6 of whom were project team members), or the project team. MEASUREMENTS: Quantitative data included demographic characteristics, processes of care, and scores on the Quality of End-of-Life Care-10 instrument. Semistructured interviews of family members and clinicians were transcribed verbatim, and qualitative description was used to analyze them. RESULTS: Participants included 40 decedents, at least 1 family member per patient, and 3 clinicians per patient. The 159 wishes were implemented and classified into 5 categories: humanizing the environment, tributes, family reconnections, observances, and "paying it forward." Scores on the Quality of End-of-Life Care-10 instrument were high. The central theme from 160 interviews of 170 persons was how the 3 Wishes Project personalized the dying process. For patients, eliciting and customizing the wishes honored them by celebrating their lives and dignifying their deaths. For families, it created positive memories and individualized end-of-life care for their loved ones. For clinicians, it promoted interprofessional care and humanism in practice. LIMITATION: Impaired consciousness limited understanding of patients' viewpoints. CONCLUSION: The 3 Wishes Project facilitated personalization of the dying process through explicit integration of palliative and spiritual care into critical care practice. PRIMARY FUNDING SOURCE: Hamilton Academy of Health Science Research Organization, Canadian Intensive Care Foundation.

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)0.017
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0030.002
Open science0.0020.010
Research integrity0.0010.003
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 head0.467
GPT teacher head0.510
Teacher spread0.043 · 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 designObservational
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

Citations105
Published2015
Admission routes2
Has abstractyes

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