Adherence to the AAP’s Institutional Ethics Committee Policy Recommendations
Bibliographic record
Abstract
OBJECTIVES: In 2019, the American Academy of Pediatrics (AAP) outlined 8 operational recommendations for pediatric institutional ethics committees (IECs). The study purpose was to quantify the extent to which pediatric IECs adhere to the AAP IEC Policy Statement recommendations. METHODS: A convenience sample of ethics points of contact from Children's Hospital Association membership were invited to complete an electronic survey on their ethics programs and practices in spring 2022. Nineteen survey questions were preidentified as reflecting measures specific to best practice standards previously published by the AAP. This subset of questions was analyzed using frequencies and categorized to assess for adherence to the AAP IEC policy recommendations. RESULTS: A total of 117 out of 181 surveys were completed (65%). Stark IEC practice gaps include: lack of diversity of membership, training needs to maintain members' competencies, quality improvement within the organization, and scope of ethics service. Over one-quarter of IECs do not have a systematic way of informing hospital staff about ethics consultancy services and how to place an ethics consult. Nineteen percent of responding IEC services do not inform patients or families about the existence of ethics consult services. One-third of responding children's hospitals do not provide resources for the IECs to engage in ethics education at the facility. CONCLUSIONS: IECs in children's hospitals are not consistently abiding by operational recommendations. Next steps should include assessment of recommendation barriers and enablers with a goal of enhancing strong practices across IECs in children's hospitals.
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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 | 0.188 | 0.331 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".