Empowering Self-Management of Chronic Low Back Pain Among Spanish and Cantonese Speakers in the United States
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain (cLBP) is a major cause of disability worldwide and disproportionately affects patients with limited English proficiency (LEP) who face linguistic, cultural, and socioeconomic barriers to care. Spanish- and Cantonese-preferring populations in the U.S. often struggle with limited access to culturally appropriate resources, highlighting the need for patient-centered approaches. METHODS: We conducted a qualitative study at an urban, academic-affiliated county hospital between January and June 2024. Focus groups were facilitated by bilingual, bicultural researchers. The study objective was to explore the priorities, barriers, and self-management preferences of Spanish- and Cantonese-preferring patients with the human-centered design (HCD) approach. Transcripts were translated, reviewed, and analyzed inductively to identify key themes. Institutional Review Board (IRB) approval to interview fifteen participants was obtained prior to study initiation. RESULTS: Fifteen Spanish- and Cantonese-preferring patients with cLBP participated in six focus groups. Participants reported 6.82 on the numerical rating scale of pain (NRS) (SD 2.49), with 71% of Spanish-speaking and 78% of Cantonese-speaking participants reporting 10/10 "complete trust" in their healthcare providers. Thematic analysis revealed four key themes: the need for empathic, tailored educational supports; desire for plans that reflect social and economic realities; recognition of mental health and social isolation as contributors to pain; and a need for clearer, trustworthy guidance on self-management. Participants preferred plain-language, video-based resources and support in understanding cLBP causes and management. Across both groups, patients expressed confusion about trustworthy information sources and called for clinician-vetted guidance and clearer explanations of self-management strategies. CONCLUSIONS: Spanish- and Cantonese-preferring patients with cLBP face significant barriers to self-management and would benefit from culturally and linguistically appropriate resources. This study highlights the need for healthcare systems to develop and deliver tailored, accessible self-management support materials that address the unique challenges faced by patients with LEP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".