Impact of e-learning and role play-based training on psychology students’ communication skills: a feasibility study
Bibliographic record
Abstract
Background Given the importance of communication skills in the psychologist-patient relationship, several training programs have been proposed. Cumulative microtraining (CMT) has shown positive impacts on communication skills in previous studies.Methods The aim of this naturalistic pre–post study was to test the feasibility of a hybrid CMT program and obtain preliminary data on its impact on communication skills in French-speaking third-year psychology students. The training included an e-learning curriculum and role plays. Pre–post measures included recorded peer-to-peer role plays and self-assessments by participants themselves using the Calgary Cambridge Grid (n = 38) and assessed by an independent rater (n = 29) with a checklist focused on objective behaviors and the CARE questionnaire measuring perceived empathy.Results The results showed increases in most communication skills at different levels. Summarizing, paraphrasing, and structuring skills were significantly increased after training (all P ≤ 0.001), as were self-reported measurements (all P < 0.001), and empathy and confidence assessed by an independent rater (all p < 0.05).Conclusion This study provides new evidence on the impact of CMT, including e-learning and role plays, on both self-rated assessments and assessments by an independent rater who measured communication and empathy in a population of French-speaking students. These findings highlight the importance of implementing such instruction in initial training despite the cost involved. It demonstrates the feasibility of its inclusion in university curriculum, facilitated by the adaptation of theoretical aspects of teaching in e-learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".