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Record W2518165386 · doi:10.1891/1933-3196.10.3.135

EMDR Therapy and Psycho-Oncology

2016· article· en· W2518165386 on OpenAlexaff
Louise Maxfield

Bibliographic record

VenueJournal of EMDR Practice and Research · 2016
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsWestern University
Fundersnot available
KeywordsEye movement desensitization and reprocessingMedicineAnxietyDepression (economics)DiseasePsychotherapistQuality of life (healthcare)CancerPopulationIntensive care medicinePsychiatryPsychologyInternal medicineNursingPosttraumatic stress

Abstract

fetched live from OpenAlex

Cancer is not only a debilitating disease; it can also have devastating effects on a patient’s mental health and quality of life. Although the literature shows that mortality rates may be improved with the provision of effective treatment, most psychotherapy provided to patients with cancer tends to be quite generic and not always effective. Very few research studies have investigated the provision of trauma-focused therapies to this underserved population. Eye movement desensitization and reprocessing (EMDR) therapy has well-established efficacy in the treatment of traumatic stress and preliminary evidence in the treatment of depression and anxiety. It is a very effective and accessible treatment for patients with cancer. Because it does not require homework, it is less demanding than many other forms of treatment. It can be provided on an intensive (twice) daily basis, making it available to patients traveling from out of town for their cancer treatment. For those patients unable to manage trauma-focused treatment during a difficult time, it can be used to enhance personal resources. EMDR therapy can be administered in individual, group, and couple formats and is suitable for children as well as adults. New research provides promising results for the application of EMDR for patients with cancer.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.376
GPT teacher head0.594
Teacher spread0.218 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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