Initiating and integrating a personalized end of life care project in a community hospital intensive care unit: A qualitative study of clinician and implementation team perspectives
Bibliographic record
Abstract
RATIONALE: The end of life (EOL) experience in the intensive care unit (ICU) can be psychologically distressing for patients, families, and clinicians. 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 implementation team perspective. METHODS: This qualitative descriptive 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. Following the qualitative descriptive approach, semi-structured interviews were conducted with purposively sampled clinicians and implementation team. Data from transcribed interviews were analyzed in triplicate through qualitative content analysis. RESULTS: Interviews with 12 participants indicated that the 3WP personalized and enriched the EOL experience. Interviewees indicated higher intensity education strategies were needed to enable spread as the project grew. Clinicians described many physical resources for the project but suggested more non-clinical project support for orientation, continuing education, and data collection. A majority of wishes focused on physical resources including keepsakes, which helped facilitate project spread when clinician capacity was attenuated by competing duties. CONCLUSIONS: In this community hospital, ICU clinicians and implementation team members report perceived improved EOL care for patients, families, and clinicians following 3WP initiation and integration. Implementing individualized and meaningful wishes at EOL for dying patients in a community ICU requires adequate planning and time dedicated to optimizing clinician education. Adapting key features of an intervention to local expertise and capacity may facilitate spread during project initiation and integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".