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Record W4408537932 · doi:10.1080/13548506.2025.2478517

Underserved older adults’ treatment preferences for a mind-body activity program for chronic pain delivered via shared medical visits in a community clinic

2025· article· en· W4408537932 on OpenAlexfundno aff
Alex Presciutti, Madison Ehmann, Nadine Levey, Julie Brewer, Christina L. Rush, Jonathan Greenberg, K. Mcdermott, Christine S. Ritchie, Ana‐Maria Vranceanu

Bibliographic record

VenuePsychology Health & Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersInstitute of AgingNational Center for Complementary and Integrative HealthNational Institute of Neurological Disorders and Stroke
KeywordsChronic painMedicineGerontologyPhysical therapyFamily medicinePsychology

Abstract

fetched live from OpenAlex

Older adults with chronic pain from underserved communities need evidence-based pain management programs. To meet this need, we interviewed patients and staff from an underserved community clinic to identify their treatment preferences and barriers and facilitators to participating in a mind-body activity program. We conducted nine qualitative interviews (two staff; seven patients) and six focus groups (three staff groups; three patient groups), transcribed them verbatim and then used inductive-deductive thematic analysis guided by two pre-specified superordinate domains: (1) treatment preferences and (2) barriers and facilitators to participation. Participants recommended flexible, group participation options (in person, remote) with a credible leader and with multi-cultural considerations. They generally reacted positively to the proposed content. Barriers included logistical barriers (e.g. transportation, finances), weather, and skepticism about novel treatments; facilitators centered on expanding access and increasing sense of community. Our findings highlight important considerations to facilitate the uptake of mind-body activity programs for underserved older adults with chronic pain.

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)0.005
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.471
Teacher spread0.411 · 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
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

Citations1
Published2025
Admission routes1
Has abstractyes

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