Exploring Early Perceptions and Experiences of ChatGPT in Pediatric Critical Care: A Qualitative Study Among Healthcare Professionals
Bibliographic record
Abstract
Abstract This qualitative inquiry explores the initial impressions and firsthand encounters of healthcare professionals (HCPs) with ChatGPT, a Generative Pre-trained Transformer, within Pediatric Intensive Care Units (PICUs). Through focus group discussions held at a tertiary academic center, a diverse cadre of HCPs was engaged to ascertain their awareness, utilization patterns, perceived advantages, and apprehensions regarding ChatGPT. The analysis revealed three primary themes: understanding and ease of use of ChatGPT, its practical applications in clinical workflows for critically ill children and information retrieval, and the ethical considerations associated with its deployment. While participants praised ChatGPT for its engaging interface and potential to streamline tasks and provide prompt information, notable reservations surfaced regarding its limitations, particularly in medical accuracy, currency of data, and ethical implications. The findings suggest a cautious optimism towards integrating Generative Artificial Intelligence (GAI), like ChatGPT, in pediatric critical care, highlighting the need for balanced, informed, and transparent applications, with ongoing evaluation of GAI technologies in pediatric healthcare settings.
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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".