The impact of humanoid robot presence in the paediatric emergency department waiting room: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The waiting room represents families' first point of contact with the emergency department (ED). We wished to study if a humanoid robot technology presence in the waiting room would improve satisfaction and decrease anxiety for caregivers in the paediatric ED. METHODS: This observational cohort study was conducted from September to December 2018 at a Canadian paediatric ED. All caregivers and children >11 years in the ED waiting room were eligible. We compared a robotic intervention (RI) to standard of care (SOC) education in the waiting room. The RI was a 5-minute psychoeducational program describing the ED process and flow. Specific days were designated for RI or SOC. An anonymous survey was administered twice, at the same times, on both SOC and RI days. The primary outcomes were (a) caregiver satisfaction with waiting room experience using a 5-point Likert scale; and (b) caregiver-reported anxiety in the waiting room, as measured by the State Trait Anxiety Inventory - State Scale. RESULTS: Six hundred and thirty-three caregivers participated, with a median age of 37 years (IQR 32 to 42); 80 children participated, with a median age of 15 years (IQR 13 to 16). Caregivers reported greater overall satisfaction in the RI cohort (174/200, 87.0%) compared to the SOC cohort (144/229, 62.9%; P<0.0001). Caregivers also reported lower anxiety in the RI cohort (39.38±11.38) compared to the SOC cohort (42.04±11.99; P=0.009). CONCLUSIONS: A humanoid robot-based psycho educational intervention in the paediatric ED waiting room has a positive impact on caregiver satisfaction and anxiety.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".