Survey on Barriers to Critical Care and Palliative Care Integration
Bibliographic record
Abstract
Purpose:It has been shown that integrating palliative care (PC) in intensive care unit (ICU) improves end-of-life care (EOLC), but very few Canadian hospitals have adopted this practice. Our study aims to evaluate the perceived quality of EOLC at participating institutions and explore barriers toward ICU-PC integration.Materials and Methods:A self-administered questionnaire was developed by a multidisciplinary team. Survey items were extracted from published quality indicators in EOLC and barriers to ICU-PC integration. The study took place at 2 academic institutions. Participants consisted of physicians and nurses, ICU administrators, and allied health workers.Results:An overall response of 45% was achieved. Of total, 85% of the respondents were ICU nurses. The following main themes were identified: (1) There is a poor presence of PC in the ICU and 78% of respondents felt that increasing ICU-PC integration will improve quality of EOLC; (2) the main barrier to integration was unrealistic patient and/or family expectations; and (3) criteria-triggered consultation to PC was the most feasible way to achieve integration.Conclusion:Our findings indicate that the majority of respondents perceive that the presence of PC in ICU will improve EOLC. Future quality improvement initiatives can focus on developing a set of criteria for triggering PC consults.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".