Cognitive Load in Pediatric Critical Care Medicine: Tsunamis and a Thousand Cuts
Bibliographic record
Abstract
IMPORTANCE: Excessive cognitive load impairs task performance and contributes to burnout, but studies of cognitive load in pediatric critical care medicine (PCCM) settings are limited. OBJECTIVES: To better understand cognitive load in an academic PCCM setting and how cognitive load differs based on experience, role, task type, and task frequency. DESIGN, SETTINGS, AND PARTICIPANTS: Prospective two-part survey at a quaternary children's hospital PCCM department. Part 1 (February to March 2022) assessed routine role-specific tasks; part 2 (June to August 2022) evaluated acute resuscitation. Participants were registered nurses (RNs), respiratory therapists (RTs), and physicians + advanced practice providers (APPs). MAIN OUTCOMES AND MEASURES: Raw cognitive load (1-9 Paas scale), net cognitive load (Paas × task frequency), and NASA-Task Load Index (NASA-TLX) subdomain scores (0-100) for acute resuscitation. Role was the primary exposure; between-group differences were analyzed using analysis of variance with pairwise comparisons. RESULTS: There were 109-part 1 and 79-part 2 survey respondents. Across all tasks, mean raw Paas scores were highest for physicians + APPs (5.2 ± 1.1), followed by RNs (4.8 ± 1.0) and RTs (4.0 ± 1.4; p = 0.004). In the three highest-load shared tasks-acute resuscitation, rescuing a decompensating patient, and managing advanced life-support devices-RNs reported significantly higher raw load than physicians + APPs and RTs. For bedside patient assessment, RNs had higher net cognitive load (25.0 ± 8.7) than physicians + APPs (20.3 ± 7.0; p = 0.01) and RTs (18.9 ± 8.9; p = 0.01). Nursing experience correlated with overall net cognitive load (r = 0.30; p = 0.02). During resuscitation, RNs reported higher NASA-TLX scores than other providers in all but two subdomains. CONCLUSIONS AND RELEVANCE: Cognitive load in PCCM varies significantly by role and task type. Nurses experience high raw cognitive load from critical events and net cognitive load from bedside patient assessment, suggesting opportunities for role-specific workflow redesign and cognitive load reduction strategies to benefit staff and patients.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".