Palliative Care in Survivors of Critical Illness: A Qualitative Study of Post-Intensive Care Unit Program Clinicians
Bibliographic record
Abstract
Background: Survivors of critical illness experience high rates of serious health-related suffering. The delivery of palliative care may assist in decreasing this burden for survivors and their families. Objectives: To understand beliefs, attitudes, and experiences of post-intensive care unit (ICU) program clinicians regarding palliative care and explore barriers and facilitators to incorporating palliative care into critical illness survivorship care. Design: Qualitative inquiry using semistructured interviews and framework analysis. Results were mapped using the Consolidated Framework for Implementation Research. Setting/Subjects: We interviewed 29 international members (United States, United Kingdom, Canada) of the Critical and Acute Illness Recovery Organization post-ICU clinic collaborative. Results: All interprofessional clinicians described components of palliative care as essential to post-ICU clinic practice, including symptom management, patient/family support, facilitation of goal-concordant care, expectation management and anticipatory guidance, spiritual support, and discussion of future health care wishes and advance care planning. Facilitators promoting palliative care strategies were clinician level, including first-hand experience, perceived value, and a positive attitude regarding palliative care. Clinician-level barriers were reciprocals and included insufficient palliative care knowledge, lack of self-efficacy, and a perceived need to protect ICU survivors from interventions the clinician felt may adversely affect recovery or change the care trajectory. System-level barriers included time constraints, cost, and lack of specialty palliative care services. Conclusion: Palliative care may be an essential element of post-ICU clinic care. Implementation efforts focused on tailoring strategies to improve post-ICU program clinicians' palliative care knowledge and self-efficacy could be a key to enhanced care delivery for survivors of critical illness.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".