A survey of pediatric intensive care unit clinician experience with restricted family presence during COVID-19
Bibliographic record
Abstract
PURPOSE: Limiting family presence runs counter to the family-centred values of Canadian pediatric intensive care units (PICUs). This study explores how implementing and enforcing COVID-19-related restricted family presence (RFP) policies impacted PICU clinicians nationally. METHODS: We conducted a cross-sectional, online, self-administered survey of Canadian PICU clinicians to assess experience and opinions of restrictions, moral distress (Moral Distress Thermometer, range 0-10), and mental health impacts (Impact of Event Scale [IES], range 0-75 and attributable stress [five-point Likert scale]). For analysis, we used descriptive statistics, multivariate regression modelling, and a general inductive approach for free text. RESULTS: Representing 17/19 Canadian PICUs, 368 of 388 respondents (94%) experienced RFP policies and were predominantly female (333/368, 91%), English speaking (338/368, 92%), and nurses (240/368, 65%). The mean (standard deviation [SD]) reported moral distress score was 4.5 (2.4) and was associated with perceived differential impact on families. The mean (SD) total IES score was 29.7 (10.5), suggesting moderate traumatic stress with 56% (176/317) reporting increased/significantly increased stress from restrictions related to separating families, denying access, and concern for family impacts. Incongruence between RFP policies/practices and PICU values was perceived by 66% of respondents (217/330). Most respondents (235/330, 71%) felt their opinions were not valued when implementing policies. Though respondents perceived that restrictions were implemented for the benefit of clinicians (252/332, 76%) and to protect families (236/315, 75%), 57% (188/332) disagreed that their RFP experience was mainly positive. CONCLUSION: Pediatric intensive care unit-based RFP rules, largely designed and implemented without bedside clinician input, caused increased psychological burden for clinicians, characterized as moderate moral distress and trauma triggered by perceived impacts on families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".