Exploring Early Perceptions and Experiences of ChatGPT in Pediatric Critical Care: A Qualitative Study Among Health Care Professionals
Bibliographic record
Abstract
To optimize the deployment of Generative Artificial Intelligence in health care, it's essential for health care professionals (HCPs) to understand these technologies' capabilities and constraints. This study explores HCPs' initial impressions and experiences using ChatGPT, a Generative Pre-trained Transformer, in Pediatric Critical Care Units (PICUs). By conducting focus groups with a diverse set of HCPs, we aimed to assess their awareness, utilization, perceived benefits, and concerns about incorporating ChatGPT into their PICUs. The discussions highlighted three main themes: familiarity and usability of ChatGPT, its role in clinical and organizational tasks, and ethical concerns. While participants appreciated ChatGPT's user-friendliness and potential to expedite tasks and provide rapid information, they expressed concerns regarding data reliability, recency, and ethical implications. Despite these reservations, there is cautious optimism about integrating these tools in PICU, underscoring the need for vigilance and ongoing evaluation of novel health care-related implications as these technologies evolve.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".