Comparing Training Methods for a New Interactive Whiteboard
Bibliographic record
Abstract
The successful implementation of health information systems can be affected by various barriers ranging from technological, human, and organizational. Training is one of the most cited factors for successful implementation. The goal of this study was to evaluate the effectiveness of various training methods. The first two levels Kirkpatrick’s Four-Level Training Evaluation model were utilized to evaluate the training approaches for four groups: No training (control), training through an instructional booklet, training through a video tutorial and super-user training. Following training, participants answered a questionnaire about their impressions of the training and were asked to complete an exercise with an interactive whiteboard. The questionnaire suggested that users preferred super-user training. Based on the results of the exercise, there was a statistically significant difference between training methods in terms of the number of correctly answer questions. Super-user and video training were significantly better compared to the control group. There were no statistically significant differences in the amount of time it took to complete the exercise. Based on these results, super-user training is recommended.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".