How Low Back Pain is Managed—A Mixed-Methods Study in 32 Countries. Part 2 of Low Back Pain in Low- and Middle-Income Countries Series
Bibliographic record
Abstract
BACKGROUND: The Lancet Low Back Pain (LBP) Series highlighted the lack of LBP data from low- and middle-income countries (LMICs). The study aimed to describe (1) what LBP care is currently delivered in LMICs and (2) how that care is delivered. DESIGN: An online mixed-methods study. METHODS: A Consortium for LBP in LMICs (n = 65) was developed with an expert panel of leading LBP researchers (>2 publications on LBP) and multidisciplinary clinicians and patient partners with 5 years of clinical/lived LBP experience in LMICs. Quantitative data were analyzed using descriptive statistics. Two researchers independently analyzed qualitative data using inductive and deductive coding and developed a thematic framework. RESULTS: Forty-seven (85%) of 55 invited panel members representing 32 LMICs completed the survey (38% women, 62% men). The panel included clinicians (34%), researchers (28%), educators (6%), and people with lived experience (4%). Pharmacotherapies and electrophysiological agents were the most used LBP treatments. The thematic framework comprised 8 themes: (1) self-management is ubiquitous, (2) medicines are the cornerstone, (3) traditional therapies have a place, (4) society plays an important role, (5) imaging use is very common, (6) reliance on passive approaches, (7) social determinants influence LBP care pathway, and (8) health systems are ill-prepared to address LBP burden. CONCLUSION: LBP care in LMICs did not consistently align with the best available evidence. Findings will help research prioritization in LMICs and guide global LBP clinical guidelines. J Orthop Sports Phys Ther 2024;54(8):560-572. Epub 11 April 2024. doi:10.2519/jospt.2024.12406
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".