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Record W3013882896 · doi:10.3389/fnhum.2020.00086

The Adoption of New Treatment Modalities by Health Professionals and the Relative Weight of Empirical Evidence in Favor of Virtual Reality Exposure Versus Mindfulness in the Treatment of Anxiety Disorders

2020· review· en· W3013882896 on OpenAlexafffund
Kévin Nolet, Giulia Corno, Stéphane Bouchard

Bibliographic record

VenueFrontiers in Human Neuroscience · 2020
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversité du Québec en Outaouais
FundersCanada Research Chairs
KeywordsMindfulnessAnxietyModalitiesTreatment modalityPsychologyHealth professionalsPsychotherapistEmpirical evidenceClinical psychologyPsychiatryMedicineHealth careInternal medicineEconomicsEpistemology

Abstract

fetched live from OpenAlex

Anxiety disorders are among the most prevalent mental disorders, and cognitive-behavioral therapy (CBT) with exposure exercises is considered as the gold-standard psychological intervention. New psychotherapeutic modalities have emerged in the last decade and, among them, mindfulness has been rapidly adopted by therapists. The adoption rate is slower for the use of virtual reality (VR) to conduct exposure. The goal of the present position paper is to contrast, for the treatment of anxiety disorders, the weight of empirical evidences supporting the use of exposure in VR with the use of mindfulness-based therapy (MBT). Based on the most recent meta-analyses, we found that CBT with exposure conducted in VR was more thoroughly researched and supported than MBT, receiving validation from roughly twice as many studies with high control (i.e., randomized, active controls with clinical samples). However, this conclusion is nuanced by reviewing gaps in the literature for both therapies. Potential factors influencing clinicians' choice of treatment and suggestions for future research directions are proposed.

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)0.001
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.170
GPT teacher head0.438
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 teacher head, not a consensus.

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

Citations11
Published2020
Admission routes2
Has abstractyes

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