Cultivating Comfort: Examining Participant Satisfaction with Hypnotic Communication Training in Pain Management
Bibliographic record
Abstract
Context: One in four Canadians experiences chronic pain, yet insufficient services and restrictions surrounding prevailing treatments result in inadequate management and significant negative consequences for these individuals. Previous work indicates that hypnotic communication represents a promising complementary treatment; however, training protocols for healthcare professionals are underdeveloped and understudied. Aim: To evaluate the level of satisfaction for a training program on hypnotic communication in pain management clinics. Design: Qualitative study. Methods: Six health professionals who first completed the hypnotic communication training participated in 30 minutes virtual semi-structured interviews. These testimonials allowed them to elaborate on their user experience and potential areas for improvement. Thematic analysis using qualitative data management software NVIVO was conducted on the interview data. Results: Two themes emerged from the interviews. 1) Satisfaction: Participants expressed satisfaction on various structural aspects of the training, including the provided materials, atmosphere, training structure, presentation modalities, practical workshops, acquired knowledge, trainer quality, and training duration. 2) Areas for Improvement: Five main improvement suggestions were identified (providing more material; more practical workshops, more concrete and adapted; testimonials from former patients; follow-up training meeting; and continuing education). Implications for the Profession and/or Patient Care and Conclusion: The results improved the training program to help minimized inherent biases related to this technique, cut associated costs, and identify reasons that would explain its underutilization among medical professionals in Quebec. Our work highlights that healthcare professionals in chronic pain management clinics (eg, respiratory therapists, nurses) can incorporate this simple hypnotic communication technique into their usual care and contribute to the well-being of patients. Impact: This study aimed to address the lack of training protocols for healthcare professionals, that are underdeveloped and understudied. The main findings on participant' satisfaction and the areas of improvement for the training will help the refinement of the training to better suit healthcare professional's needs in hospitals and chronic pain facilities.
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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 | 0.008 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".