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Record W2768970965 · doi:10.21037/apm.2017.08.15

Cognitive hypnotherapy for psychological management of depression in palliative care

2018· review· en· W2768970965 on OpenAlexaff
Assen Alladin

Bibliographic record

VenueAnnals of Palliative Medicine · 2018
Typereview
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePalliative carePsychosocialDepression (economics)Management of depressionPsychiatryCognitionPsychotherapistNursingFamily medicinePsychologyPrimary care

Abstract

fetched live from OpenAlex

The prevalence of psychiatric disorders in palliative care is well documented, yet they often remain undetected and untreated, adding further to the burden of suffering on patients who are already facing severe physical and psychosocial problems. This article will focus on depression as it represents one of the most common psychiatric disorders treated by psychiatrists and psychotherapists in palliative care. Although depression in palliative care can be treated successfully with antidepressant medication and psychotherapy, a significant number of depressives do not respond to either medication or existing psychotherapies. This is not surprising considering depression is a complex disorder. Moreover, the presentation of depression in palliative care is compounded by the severity of the underlying medical conditions. It is thus important for clinicians to continue to develop more effective treatments for depression in palliative care. This article describes cognitive hypnotherapy (CH), an evidence-based multimodal treatment for depression which can be applied to a wide range of depressed patients in palliative care. CH, however, does not represent a finished product; it is a work in progress to be empirically validated and refined by advances in cancer and clinical depression.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.423
GPT teacher head0.537
Teacher spread0.113 · 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.

Study designOther design
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

Citations19
Published2018
Admission routes1
Has abstractyes

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