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Record W2791089504 · doi:10.1177/1367493518765220

Views of children, parents, and health-care providers on pediatric disclosure of medical errors

2018· article· en· W2791089504 on OpenAlexafffund
Donna Koller, Sherry Espin

Bibliographic record

VenueJournal of Child Health Care · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsToronto Metropolitan UniversitySickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsCLARITYStakeholderMedicineFocus groupHealth careNursingFamily medicineMEDLINEPublic relationsBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.434
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2018
Admission routes2
Has abstractyes

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