Assessing agreement on Canadian pediatric critical care training objectives in Japan
Bibliographic record
Abstract
BACKGROUND: Japan has no accredited learning objectives for pediatric critical care medicine trainees. This study examined the extent of agreement among a panel of Japanese pediatric intensive care unit (PICU) directors regarding the need for the Canadian learning objectives in pediatric critical care knowledge and technical skills training. METHODS: Using a two-round Delphi survey, we developed consensus among directors of PICUs in Japan on the Canadian training objectives in pediatric critical care medicine. To assess agreement, we applied a four-point Likert scale (1 = unnecessary, 2 = relatively unnecessary, 3 = relatively necessary, 4 = necessary). We conducted a web-based survey and an Excel-based survey over 4 week periods for the first and second rounds, respectively. Consensus was set at ≥80% agreement; items rated 3 or 4 by ≥80% participants in either rounds were included in the final list. RESULTS: Of the 36 PICU directors invited, 32 (88.9%) completed all survey rounds. In the first round, 164 items were agreed to be necessary, one item was deemed unnecessary, the directors did not reach agreement on 15 items, and these items were included in the second round. In the second round, five items were agreed to be necessary and agreement could not be reached on 10 items. Finally, there was agreement on 169 (94.9%) of the Canadian learning objectives after the two-round Delphi survey. Sixteen participants commented that non-technical skills, such as communication, collaboration, management, and education, were important additional objectives. CONCLUSIONS: Strong consensus was observed among Japanese pediatric critical care experts concerning the Canadian learning objectives for pediatric critical care knowledge and technical skills training.
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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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".