How professionals in pediatrics change the words they use to mitigate pain: A lexical description after a short hypnosis-based communication training
Bibliographic record
Abstract
Background: Young patients who suffer from a pediatric condition are typically submitted to diverse and often repeated painful procedures. Theory and empirical studies suggest that communication styles used by healthcare professionals could mitigate such procedural pain. Recently, a hypnotic communication training (Rel@x) was developed with promising results. The present study aimed to describe how healthcare professionals change the words they use with patients after training. Methods: A nine-hour training in hypnosis-derived communication was offered to 78 volunteer healthcare professionals from a tertiary pediatric hospital, and 58 participated in the evaluative study. Participants were evaluated at baseline, immediately after the training, and 5 months later (39 ± 10 yrs, 52 women, 54 nurses). We used a video-recorded standardized simulation protocol of venipuncture, and five categories of words were derived. Word categories were corroborated in a validity study with 10 independent judges. We modeled pre-post-follow-up changes over time with latent growth curve models. Results: Following training, healthcare professionals used fewer words related to negative experiences (-51%) or medical procedures (-73%) and used more words referring to the relaxing and analgesic experience (+20%), and the specific techniques they had learned (Pleasant place +260%, Magic glove +582%). These changes were maintained at a proportion of 45-81% 5 months later. More change was observed among women and less experienced healthcare professionals. Conclusion: Results suggest that healthcare professionals exposed to a short, structured communication training aiming to mitigate pediatric pain durably adjust the language they use when performing a painful procedure. This is encouraging for future testing and implementation of hypnosis-derived communication training in healthcare providers.
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How this classification was reachedexpand
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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".