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Record W4408283719 · doi:10.1038/s41598-025-91267-7

Training healthcare professionals in hypnosis-derived communication to mitigate procedural pain in children

2025· article· en· W4408283719 on OpenAlexaff
Serge Sultan, Michel Duval, Jennifer Aramideh, Beáta Bőthe, Amy Latendresse, M. Bedu, Ariane Lévesque, Émélie Rondeau, Sylvie Le May, Ahmed Moussa, Claude Julie Bourque, Argerie Tsimicalis, Evelyne D Trottier, Jocelyn Gravel, David Ogez

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityHôpital Maisonneuve-RosemontUniversité de MontréalUniversité du Québec à MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsHypnosisHealth professionalsTraining (meteorology)Health carePain managementMEDLINEMedicinePsychologyPhysical therapyAlternative medicineBiology

Abstract

fetched live from OpenAlex

How professionals communicate during medical procedures may have a significant impact on children and adolescents' pain. Rel@x is a manualized training program designed to develop hypnosis-derived communication skills to mitigate childhood pain and distress. The study aimed to evaluate if this training was associated with an improvement and maintenance in communication skills over time, and measure associations between changes and participants' characteristics. A 9-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 training, and 5 months later (39 ± 10 yrs, 52 women, 54 nurses). We used a video-recorded standardized simulation protocol of blood draw and coded the participants' interactions with the pre-validated Sainte-Justine Hypnotic Communication Assessment Scale (SJ-HCAS) assessing relational, technical, and total skills. We modeled pre-post-follow-up changes over time with latent growth curve models. Satisfaction with Rel@x was consistently excellent (97%). Across the 3 domains, we observed significant improvements of total (+ 61%, 95% CI 53-69%), relational (+ 27%, 95% CI 20-34%), and technical skills (+ 124%, 95% CI 08-140%). Post-training competence levels were 73-91% across domains. A large proportion of acquired skills were maintained at 5 months (55-75%) suggesting a significant effect of the training. Sensitivity analyses confirmed these results (best-case/worst-case skill maintenance ratio: 59-79%/49-73%). Larger improvements in technical skills were associated with younger age and lower baseline skills of participants. The Rel@x training is associated with improved skills in hypnotic communication post-training and at follow-up. This simulation study paves the way for future efficacy studies to examine the effect of hypnotic communication on real patients' pain and distress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.340
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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