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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".