How Fast Can Nurses Learn Therapeutic Communication Skills? A Pilot Study on Brief Hypnotic Communication Training Conducted with Oncology Nurses
Bibliographic record
Abstract
Objective: This project aimed to train nurses on an oncology unit in hypnotic communication to reduce treatment-related pain and anxiety in their patients. A pilot study was conducted to assess changes in hypnotic communication behaviors associated with the training. Methods: Nurses were recruited and their interactions during a simulated patient admission for treatment (before and after training) were recorded. Hypnotic communication skills were assessed by independent reviewers using a training checklist listing different hypnotic communication techniques and a validated assessment scale (Sainte-Justine Hypnotic Communication Assessment Scale, SJ-HCAS). Results: Seven nurses were evaluated. Wilcoxon paired-sample tests (pre–post) reported significant improvement with large effect sizes in the total score of the training grid (P = 0.034, r = 0.832) and significant improvement with large effect sizes in the relational (P = 0.018, r = 0.930) and total (P = 0.021, r = 0.903) scores of the SJ-HCAS. Conclusion: This pilot study shows promising results regarding the effectiveness of hypnotic communication training for nurses. These acquired skills could translate into improved treatment experience with patients and could be transferred to other professionals and settings in the health care system.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".