Bridging the knowledge-resuscitation gap for children: Still a long way to go
Bibliographic record
Abstract
The American Heart Association, along with the International Liaison Committee on Resuscitation, recently made changes to the paediatric resuscitation guidelines.Knowledge translation (KT) is imperative, but there is a lack of sufficient evidence for appropriate methodologies for implementation of these guidelines. Paediatric resuscitation presents many challenges; cases happen infrequently, affording few opportunities for implementation of the new guidelines, and are highly stressful and filled with uncertainty. Some KT strategies have shown some success in causing a notable degree of change in behaviour, but none have shown a striking difference when used alone.Previous efforts to disseminate current guidelines centred on development of courses for health care providers and preparing paediatric residents and paediatricians for circumstances they could encounter with paediatric acute illness. None of the studies assessing these techniques measured direct patient outcomes, and only a few demonstrated some long-term knowledge acquisition among trainees. The purpose of the present review was to illuminate the challenges, offer future directions for KT and outline potentially more effective methodologies and strategies to overcome current barriers.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".