Interventions to Improve Trainers' Learning and Behaviors for Educating Health Care Professionals Using Train-the-Trainer Method: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Train-the-trainer (TTT) programs are frequently used to facilitate knowledge dissemination. However, little is known about the effectiveness of these programs. Therefore, we sought to assess the impact of TTT programs on learning and behavior of trainers for educating health and social professionals (trainees). METHODS: Guided by the Cochrane Effective Practice and Organisation of Care, we conducted a systematic review. We searched 12 databases until April 2018 and extracted data according to the Population, Intervention, Comparison, Outcome model. Population was defined as trainers delivering training program to health care professionals, and the intervention consists in any organized activity provided by a trainer. There were no restrictive comparators, and outcomes were knowledge, attitude, skill, confidence, commitment, and behavior of trainers. We estimated the pooled effect size and its 95% confidence interval using a random-effect model. We performed a narrative synthesis when meta-analysis was not possible. RESULTS: Of 11,202 potentially eligible references, we identified 16 unique studies. Studies were mostly controlled before-and-after studies and covered a unique training intervention. Targeted trainers were mostly nurses (n = 10) and physicians (n = 5). The most frequent measured outcome was knowledge (n = 12). TTT programs demonstrated significant effect on knowledge (Standardized mean deviation = 0.58; 95% CI = 0.11-1.06; I2 = 90%; P < .01; 10 studies). No studies measured trainers' ability to deliver the training program. DISCUSSION: TTT programs may improve the knowledge of trainers. However, the heterogeneity and small number of studies hamper our ability to draw conclusions that are more robust.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".