The development of the PARENTS: a tool for parents to assess residents’ non-technical skills in pediatric emergency departments
Bibliographic record
Abstract
BACKGROUND: Parents can assess residents' non-technical skills (NTS) in pediatric emergency departments (EDs). There are no assessment tools, with validity evidence, for parental use in pediatric EDs. The purpose of this study was to develop the Parents' Assessment of Residents Enacting Non-Technical Skills (PARENTS) educational assessment tool and collect three sources of validity evidence (i.e., content, response process, internal structure) for it. METHODS: We established content evidence for the PARENTS through interviews with physician-educators and residents, focus groups with parents, a literature review, and a modified nominal group technique with experts. We collected response process evidence through cognitive interviews with parents. To examine the internal structure evidence, we administered the PARENTS and performed exploratory factor analysis. RESULTS: Initially, a 20-item PARENTS was developed. Cognitive interviews led to the removal of one closed-ended item, the addition of resident photographs, and wording/formatting changes. Thirty-seven residents and 434 parents participated in the administration of the resulting 19-item PARENTS. Following factor analysis, a one-factor model prevailed. CONCLUSIONS: The study presents initial validity evidence for the PARENTS. It also highlights strategies for potentially: (a) involving parents in the assessment of residents, (b) improving the assessment of NTS in pediatric EDs, and
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".