Acute Care of At-Risk Newborns (ACoRN): quantitative and qualitative educational evaluation of the program in a region of China
Bibliographic record
Abstract
BACKGROUND: The Acute Care of at-Risk Newborns (ACoRN) program was developed in Canada for trained health care providers for the identification and management of newborns who are at-risk and/or become unwell in the first few hours or days after birth. The ACoRN process follows an 8-step framework that enables the evaluation and management of babies irrespective of the experience or expertise of the caregiving individual or team. This study assesses the applicability of the program to Chinese pediatric practitioners. METHODS: Course content and educational materials were translated from English into Chinese by bilingual neonatal practitioners. Confidence and knowledge questionnaires were developed and reviewed for face and content validity by a team of ACoRN instructors. Bilingual Chinese instructors were trained at the tertiary perinatal centre in Hangzhou Zhejiang to deliver the course at 15 level II county hospitals. Participants completed pre- and post-course confidence and knowledge questionnaires and provided feedback through post-course focus groups. RESULTS: 216 physicians and nurses were trained. Confidence and knowledge relating to neonatal stabilization improved significantly following the courses. Participants rated course utility and function between 4.2 and 4.6/5 on all items. Pre/post measures of confidence were significantly correlated with post course knowledge. Focus group data supported the perceived value of the program and recommended course adjustments to include pre-course reading, and increased content related to simulation, communication skills, and management of respiratory illness and jaundice. CONCLUSIONS: ACoRN, a Canadian educational program, appears to be well received by Chinese health care providers and results in improved knowledge and confidence. International program adaptation for use by health care professionals requires structured and systematic evaluation to ensure that the program meets the needs of learners, reflects their learning styles, and can be applied in their setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".