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Record W4408396047 · doi:10.1177/23743735241311752

Patients’ Experiences Participating Within an Interdisciplinary Primary Care Program for Low Back Pain

2025· article· en· W4408396047 on OpenAlexafffund
Amédé Gogovor, Matthew Hunt, Richard Hovey, Sara Ahmed

Bibliographic record

VenueJournal of Patient Experience · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcGill University Health CentreCentre for Interdisciplinary Research in RehabilitationMcGill UniversityUniversité Laval
FundersFonds de Recherche du Québec - Santé
KeywordsPrimary careFlexibility (engineering)NursingPsychologyMedicineQualitative researchInterpretative phenomenological analysisLow back painMedical educationAlternative medicineFamily medicineSociology

Abstract

fetched live from OpenAlex

A common recommendation to improve the management of low back pain (LBP) is the use of interdisciplinary teams. However, many challenges remain in establishing interdisciplinary care, particularly in community-based primary care settings. This study explored patients' experiences with interdisciplinary care for LBP using an applied phenomenological research approach. Semistructured open-ended interviews were conducted with fifteen adults enrolled in a 6-month interdisciplinary LBP program within an integrated care network. The analysis included detailed descriptions of participants' experiences and interpretations by the researchers of the main themes: (i) challenging start-"It's intimidating," (ii) desire for flexibility-"I didn't need as much," (iii) better collaboration-"They are all together," (iv) grasping the pain issue-"They helped," (v) care was responsive to needs and experience-"Always centered on me, not general," (vi) meanings of recovery-"I'm able to function." Participants viewed the interdisciplinary LBP program as the culmination of a long journey toward recovery. The findings identified as important to patients contribute to our understanding of how to optimize patient-centered care for individuals living 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.353
Teacher spread0.339 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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