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Record W4399756587 · doi:10.1002/capr.12785

Differential effects of a brief body scan session on pain and anxiety levels

2024· article· en· W4399756587 on OpenAlexaff
Geneviève Bouchard, Janelle Gallant

Bibliographic record

VenueCounselling and Psychotherapy Research · 2024
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsSession (web analytics)AnxietyPsychologyDifferential effectsClinical psychologyPhysical medicine and rehabilitationPhysical therapyMedicinePsychiatryComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objective This study aimed to examine the impact of a brief body scan session on individuals' anxiety and pain levels considering individuals' levels of symptom severity (low vs. high). Method The sample was composed of 355 undergraduate or graduate students. Participants completed a series of questionnaires, performed a 14‐min body scan exercise and completed some of the questionnaires a second time. Two questionnaires were aimed at identifying individuals reporting high and low levels of symptomatology (i.e. anxiety and somatic symptoms) in their everyday life, while other questionnaires were aimed at assessing the effectiveness of the body scan session in reducing current symptoms. Results As hypothesised, body scanning was more effective in decreasing anxiety and pain for students with high‐symptom severity than for those with low‐symptom severity. Discussion We demonstrated that individuals with high levels of symptoms, and especially anxiety symptoms, can benefit from a brief form of body scan.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.419
Teacher spread0.357 · 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 designNon-randomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

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