E‐Learning Training to Improve Pediatric Parenteral Nutrition Practice: A Pilot Study in Two University Hospitals
Bibliographic record
Abstract
BACKGROUND: Education and training may improve the prescription of pediatric parenteral nutrition. The aim was to evaluate the impact of an e-learning method on parenteral nutrition prescription skills among pediatric residents in 2 pediatric hospitals. METHODS: A randomized double-blind control study was conducted over a 9-month period among pediatric residents in HOSP1, Geneva, Switzerland, where physicians prescribe parenteral nutrition directly, and in HOSP2, Montreal, Canada, where physicians prescribe only occasionally because clinical pharmacists are devoted to this activity. The intervention consisted of an e-learning session about key issues of parenteral nutrition. Physician parenteral nutrition knowledge was evaluated with a standardized questionnaire based on clinical cases before and after the e-learning in the intervention groups; in the control groups, only the 2 tests were conducted. In HOSP1, participants also underwent iterative tests every 2 months to measure the retention of knowledge. RESULTS: Sixty-five physicians participated. Initial knowledge scores were higher in HOSP1 (pretest scores 180 ± 29 vs 133 ± 24, p < 0.001). Overall, there was no significant difference in the impact of the e-learning intervention between the control and e-learning groups (p > 0.05). A significant knowledge improvement was observed in HOSP2 in the e-learning group (p = 0.033). Iterative tests in HOSP1 showed persistence of knowledge without significant differences between the groups. E-learning satisfaction among the participants was outstanding (100%). CONCLUSION: E-learning seems to be an effective method for teaching parenteral nutrition among pediatric residents and fellows at the beginning of the training. High satisfaction with this teaching method was observed in this study.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".