Assessing patient education materials about low back pain for understandability, actionability, quality, readability, accuracy, comprehensiveness, and coverage of information about patients’ needs
Bibliographic record
Abstract
BACKGROUND: Patients have unhelpful beliefs about low back pain (LBP), which are associated with worse outcomes. Education may modify these beliefs, but patients with LBP rarely receive education in practice. Patient education materials (PEMs) are a quick, inexpensive intervention to support information provision. OBJECTIVES: assess PEMs for understandability, actionability, quality, readability, accuracy, comprehensiveness, and coverage of information about patients' needs to identify the best PEMs for practice. METHODS: We searched published literature for PEMs tested in randomized trials or recommended in clinical guidelines. We used the Patient Education Materials Assessment Tool (PEMAT) to assess understandability and actionability, DISCERN to assess quality, the Patient Information and Education Needs Checklist for Low Back Pain (PINE-LBP) to assess information need coverage, and the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade-Level (FKGL) algorithms to assess readability. We assessed accuracy (proportion of treatment recommendations aligning with guidelines) and comprehensiveness (proportion of correctly covered guideline recommendations), and qualitatively synthesized PEM content relating to 21 information and education needs about LBP. RESULTS: Nineteen PEMs were included. None were actionable or comprehensive, and many had inaccurate treatment recommendations. There was considerable variation and conflicting information in the content provided across PEMs. Only the My Back Pain website met acceptable standards for more than half (4/7) outcomes. CONCLUSIONS: Educational messaging for LBP varies substantially and PEMs require improvement in various areas. The My Back Pain website met acceptable standards across most outcomes and may be the best available option for practice.
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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 | 0.031 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".