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Record W4386949811 · doi:10.1093/pm/pnad130

Virtual group psychotherapy for chronic pain: exploring the impact of the virtual medium on participants’ experiences

2023· article· en· W4386949811 on OpenAlexaff
Emily Moore, Catherine Paré, Estelle Carde, M. Gabrielle Pagé

Bibliographic record

VenuePain Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalMontreal General Hospital
Fundersnot available
KeywordsThematic analysisEmpathyContext (archaeology)PsychotherapistGroup psychotherapyPsychologyChronic painClinical psychologyQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Virtual psychotherapy for chronic pain (CP) has been shown to be feasible, efficacious, and acceptable; however, little is known about how virtual delivery of group psychotherapy affects participants' experiences. This study aimed to explore the impact of a virtual medium during the coronavirus disease 2019 (COVID-19) pandemic on social interactions and therapeutic processes in the context of group psychotherapy for CP management. METHODS: This qualitative, interview-based study collected data on 18 individuals who participated in virtual group psychotherapy in a tertiary care pain management unit. RESULTS: Results of the thematic analysis showed 4 themes. First, the ability to participate and connect was modified by not meeting in person. Connections also occurred differently as the usual patterns of interactions changed. Participants described important shifts in how emotions are communicated and subsequent experience of empathy. Finally, the commonality of chronic pain experience was identified as a central driver of connection between participants. CONCLUSIONS: Mixed impacts of the virtual medium on group psychotherapy dynamics and processes were found. Future research could explore ways to mitigate the negative impacts.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.362
Teacher spread0.297 · 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 designObservational
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

Citations10
Published2023
Admission routes1
Has abstractyes

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