Healing the Feelings and Feeling the Healing: Hypnotic Approaches in Cancer and Palliative Care
Bibliographic record
Abstract
This workshop will focus on clinical demonstrations of hypnotic suggestion through metaphors for self-regulation improvements, pain relief, palliation of symptoms, and amelioration of hope in the face of advanced or progressive illness. These materials were designed (Néron and Handel, In Press) for health care professionals who use – or may decide to use – clinical hypnosis in their professional work with patients. The role of adjunctive hypnotic therapy in cancer care is to help manage distressing symptoms and to give the sufferer a sense of control via mind-body regulation. Physicians and health care professionals can integrate personalized hypnotic approaches in order to help patients regulate physiological functions, alleviate pain, enhance the release of tension, reframe hope, facilitate new levels of personal adjustments, and promote or restore healing spiritual experiences.The workshop will include case-based, video clip demonstrations to cover the following topic areas: a) addressing patients’ misconceptions about hypnosis, b) establishing appropriate clinical goals, c) using hypnotic techniques in different medical settings, d) developing quick ways of reaching a hypnotic state, e) teaching self-hypnosis, f) preparing for medical procedures, g) reframing hope, and h) promoting healing spiritual experiences.Objectives: Participants will be introduced to ways of: a) Integrating guided clinical hypnosis procedures at bedside and in several medical contexts. b) Empowering the sufferer by teaching him or her how to use self-hypnosis for symptom relief and for addressing their existential issues.ReferenceNéron, S., and Handel, D. Hypnotic Approaches in Cancer and Palliative Care. Quebec: Presses de l’Université du Québec, In Press.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".