Development of a Patient-Oriented Intervention to Support Patient-Provider Conversations about Unnecessary Lower Back Pain Imaging
Bibliographic record
Abstract
BACKGROUND: despite the efforts of multiple stakeholders to promote appropriate care throughout the healthcare system, studies show that two out of three lower back pain (LBP) patients expect to receive imaging. We used the Choosing Wisely Canada patient-oriented framework, prioritizing patient engagement, to develop an intervention that addresses lower back pain imaging overuse. METHODS: to develop this intervention, we collaborated with a multidisciplinary advisory team, including two patient partners with lower back pain, researchers, clinicians, healthcare administrators, and the Choosing Wisely Canada lead for Saskatchewan. For this qualitative study, data were collected through two advisory team meetings, two individual interviews with lower back pain patient partners, and three focus groups with lower back pain patient participants. A lower back pain prescription pad was developed as an outcome of these consultations. RESULTS: participants reported a lack of interactive and informative communication was a significant barrier to receiving appropriate care. The most cited content information for inclusion in this intervention was treatments known to work, including physical activity, useful equipment, and reliable sources of educational material. Participants also suggested it was important that benefits and risks of imaging were explained on the pad. Three key themes derived from the data were also used to guide development of the intervention: (a) the role of imaging in LBP diagnosis; (b) the impact of the patient-physician relationship on LBP diagnosis and treatment; and (c) the lack of patient awareness of Choosing Wisely Canada and their recommendations. CONCLUSIONS: the lower back pain patient-developed prescription pad may help patients and clinicians engage in informed conversations and shared decision making that could support reduce unnecessary lower back pain imaging.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".