Views of children, parents, and health-care providers on pediatric disclosure of medical errors
Bibliographic record
Abstract
Despite the prevalence of medical errors in pediatrics, little research examines stakeholder perspectives on the disclosure of adverse events, particularly in the case of children’s own perspectives. Stakeholder perspectives, however, are integral to informing processes for pediatric disclosure. Building on a systematic review of the literature, this article presents findings from a series of focus groups with key pediatric stakeholders where perspectives were sought on the disclosure of medical errors. Focus groups were conducted with three stakeholder groups. Participants included child members of the Children’s Council from a large pediatric hospital ( n = 14), parents of children with chronic medical conditions ( n = 5), and health-care providers including physicians, nurses, and patient safety professionals ( n = 27). Children acknowledged various disclosure approaches while citing the importance of children’s right to know about errors. Parents generally identified the need for full disclosure and the uncovering of hidden errors. Health-care providers were concerned about the process of disclosure and whether it always served the best interest of the child or family. While some health-care providers addressed the need for more clarity in pediatric policies, most stakeholders agreed that a case-by-case approach was necessary for supporting variations in how medical errors are disclosed.
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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 | 0.050 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".