Interhospital variability in satisfaction with withdrawal of life support: Room for improvement?
Bibliographic record
Abstract
OBJECTIVE: To develop instruments to measure the current level of satisfaction with the withdrawal of life support among nurses and respiratory therapists and to use these instruments to determine whether differences in satisfaction levels with the withdrawal of life support could be found among different institutions. DESIGN: A multicenter, self-administered, questionnaire-based study. SETTING: The intensive care units of three academic and 11 community hospitals. SUBJECTS: A total of 412 critical care nurses and 117 respiratory therapists working in these intensive care units. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: After developing two similar questionnaires for critical care nurses and respiratory therapists, we assessed the reliability of the individual items for each questionnaire and only used those found to be reliable in the analyses. We found variability in the satisfaction level of critical care nurses among different intensive care units and among academic and community hospital intensive care units. Being involved in the plan for withdrawal of life support, comfort with the way discussions with the family had gone, comfort with patient sedation, and increasing experience with withdrawal of life support were items associated with increased nurse satisfaction. For respiratory therapists, there seemed to be similar differences among institutions in satisfaction level, although numbers were limited. Having the physician explain the plan to them and becoming more comfortable with withdrawal of life support as a result of increased experience were found to be associated with increased satisfaction levels for respiratory therapists. CONCLUSIONS: Satisfaction levels of intensive care unit nurses and respiratory therapists with the process of withdrawing life support vary among institutions. This variation may represent an opportunity for improvement in the process of withdrawing life support within this region.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".