Abstract P-333: MORAL DISTRESS IN CANADIAN PAEDIATRIC AND NEONATAL ICU: A NATIONAL SURVEY
Bibliographic record
Abstract
Aims & Objectives: The extent of moral distress (MD) experienced by Canadian Paediatric and Neonatal ICU providers is unknown. Our aim is to quantify MD, depersonalization (DP), uncertainty of treatment effect and describe demographic associations in providers. Methods Survey of Canadian neonatal and paediatric ICU providers using the Moral Distress Scale–Revised (MDS-R), Mishel’s Uncertainty Scale and Maslach Burnout Inventory to measure MD, treatment uncertainty and depersonalization (DP). Results are presented as counts (%) or median (IQR). Results 49(91%) of 54 eligible ICUs provided 2822(94%) usable responses with overall response rate of 42%. Respondents were mostly female 2626 (93%); RNs 1825(65%); fulltime 1829(65%) and from NICU 1866(66%). Overall MDS-R scores were 79 (52–113) and were similar in NICUs 79(52–114) and PICUs 79(52–114); full and part-time; and in trainees and non-trainees. Scores differed by discipline: RN 85(57–121); RT 76.5(50–113); and MD 60(41–85) (p<0.0001). Females 81(54–116) scored higher than males 63(42–100) (p<0.0001). Uncertainty scores were 11 (9–13). Each unit increase was associated with 4-unit increase in MDS-R scores (p<0.0001). DP scores were 6 (3–11). Respondents with high DP scores 633 (22%) had higher MDS-R scores 108 (76–142) (p<0.0001). DP scores increased with years of experience from 1 yr (18%), peaking at 5-9yrs (29%) and falling to 12% >30yrs. Conclusions We found higher MDS-R scores in females, RN and RRTs. The clinical relevance of MD is supported by the positive correlation found with DP. Greater uncertainty about treatment effect was associated with MD. This suggests that uncertainty may be an important determinant of MD and further methodologic work is needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".