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Record W4285799307 · doi:10.5737/23688076323394

Nurses’ perception of integrating an innovative clinical hypnosis–derived intervention into outpatient chemotherapy treatments

2022· article· en· W4285799307 on OpenAlexafffundvenue
Danny Hjeij, Karine Bilodeau, David Ogez, Marjorie Tremblay, Gilles Lavigne, Pierre Rainville, Caroline Arbour

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré de Santé et de Services Sociaux des LaurentidesHôpital du Sacré-Cœur de MontréalHôpital Maisonneuve-RosemontUniversité de Montréal
FundersUniversité de Montréal
KeywordsHypnosisMedicineNursingIntervention (counseling)Outpatient clinicPerceptionOncology nursingPsychologyFamily medicineInternal medicineAlternative medicineNurse education

Abstract

fetched live from OpenAlex

Introduction: Conversational hypnosis (CH) is known to optimize the management of symptoms resulting from antineoplastic treatment. However, the perception of nurses who have been called upon to integrate this practice into their care has yet to be documented. Goal: Describe how nurses perceive the integration of CH into chemotherapy-related care. Methods: Individual interviews and an iterative analysis were conducted with six nurses who had previous experience in CH in an outpatient oncology clinic. Findings: Six themes emerged: 1) the outpatient oncology clinic, a saturated care setting; 2) the key elements supporting the integration of CH into care; 3) an added value for patients; 4) a positive and rewarding experience for nurses; 5) collateral benefits; and 6) CH, an approach that warrants consideration amid the pandemic. Conclusion: These findings shed light on nurses' unique point of view regarding the challenges and benefits of integrating CH into oncology care.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.399
Teacher spread0.346 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2022
Admission routes3
Has abstractyes

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