Knowledge translation technologies for educating child health professionals: a scoping review
Bibliographic record
Abstract
Objective To map scientific evidence on the technological tools that have been used in knowledge translation for the education of professionals in the context of child health. Methods This is a scoping review. The JBI Manual for Evidence Synthesis recommendations were followed. Primary studies, with no time restrictions, in Portuguese, English, and Spanish were included. The search was conducted across eight databases up to November 2022. Rayyan® QCRI software was used for the screening process. Data analysis occurred in three stages: data extraction, thematic categorization, and synthesis. Results The sample included 14 studies. Canada stood out as the largest publisher of studies in this area. Most of the technologies created were soft-hard and hard, with video being the most produced technology. Conclusion The video stood out as the primary technology used in the education of healthcare professionals in pediatric settings. We can see the importance of educational technologies in health as promoters of knowledge translation and facilitators in the process of continuing education for healthcare professionals who work in child healthcare.
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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 | 0.031 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".