Comparing Training Methods for a New Interactive Whiteboard
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it