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Record W2511978800 · doi:10.1080/00029157.2016.1185004

What Can a Hypnotic Induction Do?

2016· review· en· W2511978800 on OpenAlexafffund
Erik Z. Woody, Pamela Sadler

Bibliographic record

VenueAmerican Journal of Clinical Hypnosis · 2016
Typereview
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHypnosisPsychologyInterpersonal communicationHypnoticPsychotherapistPerspective (graphical)Relevance (law)Cognitive psychologySocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

In contrast to how recent definitions of hypnosis describe the induction, a work-sample perspective is advocated that characterizes the induction as an initial, stage-setting phase encompassing everything in a hypnotic session up to the first hypnotic suggestion of particular relevance to the therapeutic or research goals at hand. Four major ways are then discussed in which the induction could affect subsequent hypnotic responses: It may provide information about how subsequent behaviors are to be enacted; it may provide cues about the nature of the interpersonal interaction to be expected in hypnosis; it may provide meta-suggestions, defined as suggestive statements intended to enhance responses to subsequent hypnotic suggestions; and it may provide a clear transition to help allow new behaviors and experiences to emerge. Several ideas for future research are advanced, such as mapping hypnosis style onto the interpersonal circumplex, evaluating whether attentional-state changes measured at the end of the induction actually mediate subsequent hypnotic responsiveness, and systematically examining the impact of ritual-like aspects of inductions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.196
GPT teacher head0.464
Teacher spread0.268 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations18
Published2016
Admission routes2
Has abstractyes

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