E-Learning: Is it any better than Traditional Approaches in Psychotherapy Education?
Bibliographic record
Abstract
Aim of the study There is a trend towards competency based psychotherapy education in psychiatry training but programs are finding it difficult to meet the expanded requirements for learner competence in multiple modalities of psychotherapy. E-learning has been raised as a potential solution to the problem. However, there have been few studies determining if e-learning is any better than traditional learning. The objective of this study is to determine if online learning modules can enhance knowledge acquisition and learner satisfaction in psychotherapy education. Subject or material and methods A need analysis was performed at this institution to determine the learning needs and preferences of the psychiatry residents. A blended course (consisting of traditional lectures, online modules and videotape review) was designed and developed based on these needs. Lectures and online modules were evaluated by means of pre-tests and post-tests. A paired t-test was used to determine if knowledge acquisition occurred in each online module and lecture group. An independent t-test was used to determine if there was greater knowledge acquisition in the online module group versus the lecture group. A learner satisfaction questionnaire was distributed with each online module. Results Nineteen residents completed the study. There was statistically significant knowledge acquisition in each online module and lecture group. There was no difference in knowledge acquisition between online modules and lectures. Learners were satisfied with the modules, but experienced minor technical difficulties. Discussion Online modules may enhance learner satisfaction in psychotherapy education. But there may be no difference in learning compared to traditional classroom-based lectures. Conclusions Further studies are needed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".