Needs assessment of ethics and communication teaching for neonatal perinatal medicine programs in Canada
Bibliographic record
Abstract
OBJECTIVE: To explore ethics education needs in Canadian Neonatal Perinatal Medicine (NPM) training programs. METHODS: A retrospective review of NPM trainees' performance at the National NPM Objective Structured Clinical Examination (OSCE) was undertaken for 2012 to 2017 and two distinct cross-sectional online surveys were carried out. One survey targeted recently graduated neonatologists (RGNs) who completed 2 years' training in a Canadian NPM program between 2010 and 2015; the other survey was sent to Canadian NPM training program directors (PDs). The domains of interest were: perception of education, ethics and communication topics, educational strategies, assessment of trainees' competencies, and barriers to neonatal ethics education. RESULTS: NPM trainees generally performed less well in stations involving ethics and communication relative to other domains on the National OSCE. Forty-seven RGNs (44.3%) and 12 PDs (92.3%) completed the survey. Over 90% of PDs and RGNs agreed on the importance of training in ethics and communication. Both groups highly valued training on topics related to communication. Preferred teaching strategies were experiential: observation and feedback. PDs mentioned the importance of using validated tools to regularly and formally assess trainees. They recognized challenges in regard to financial resources, physical space, and faculty training in patient-physician communication. CONCLUSIONS: National OSCE results indicate the need to improve neonatal ethics and communication training in Canadian NPM programs. RGNs and PDs identified important topics, as well as teaching and evaluation strategies. These results can be used to develop a training program for ethics and communication in NPM.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".