Assessment of health-care workers’ attitudes, knowledge, and skills in the care of critically ill Muslim children in New York
Bibliographic record
Abstract
OBJECTIVES: Pediatric health-care workers often care for families of minority religious backgrounds, but little is known about their perspective in providing culturally and spiritually appropriate care for Muslim patients. We aimed to (1) characterize the attitudes, knowledge, and skills of health-care workers in the care of critically ill Muslim children and (2) evaluate preferences for different educational interventions to improve care of critically ill Muslim children. METHODS: We administered a single-center, cross-sectional, 33-question, electronic survey of interdisciplinary health-care workers in a large pediatric intensive care unit in New York City to characterize their attitudes, knowledge, and skills in caring for critically ill Muslim children. RESULTS: Of 413 health-care workers surveyed, there were 109 (26%) respondents. Participants responded correctly to 51.7 ± 22.2% (mean ± SD) and 69.2 ± 20.6% of background knowledge and clinical skills questions, respectively. Only 29.8% of participants perceived adequate institutional resources to provide culturally competent care to Muslim patients and their families. Participants identified end-of-life care (47.5%) and bioethical concerns (45%) as needed areas for additional institutional resources. When asked about support to aid in caring for Muslim patients, 43.4% of participants requested a team of Muslim health-care workers to provide guidance. Participants most often requested video-based training modules (32.5%) and written materials (30%) as potential educational interventions. SIGNIFICANCE OF RESULTS: We identify gaps in health-care worker knowledge and skills in the care of the critically ill Muslim child. We also describe possible areas for intervention to facilitate culturally and spiritually appropriate care delivery to Muslim children and families.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".