Systematic quality assessment of patient education materials and decision aids for breathlessness
Bibliographic record
Abstract
Background: About 10% of individuals suffer from breathlessness. Patient education materials (PEMs) are important for shared decision making. Aims: This systematic review and environmental scan aims to assess the readability, quality and actionability of PEMs for breathlessness. Methods: PEMs published between 1 January 2010 to November 2020 were systematically obtained from CENTRAL, Embase Ovid, Pubmed, Google and 15 known decision aid repositories. Two reviewers independently assessed PEMs against the inclusion criteria, extracted data and performed quality assessment. Readability was assessed by a composite of 7 indices, understandability and actionability through the PEM Evaluation Tool (PEMAT-P), and quality against the International Patient Decision Aid Standards (IPDAS) criterias and the DISCERN tool. Results: A total of 4236 PEMs were screened and 88 PEMs analysed. The majority (51%) were for breathlessness in general, hyperventilation (22%) and 27% other diseases. Readability indices showed an average minimum reading level of Grade 10 with 35 PEMs being suitable for the general population (Grade 8) and only 1 suitable for those with low health literacy (Grade 5). PEMs scored an average of 87% for understandability and 67% for actionability. Only 5 PEMs fit the IPDAS criteria as a decision aid. Based on the DISCERN tool, 10 were classified as high quality, 55 moderate quality and 23 low quality. Conclusions: Few PEMs provided sufficient support for decision making, are of high quality and suitable for low health literacy populations. There9s a need for higher quality PEMs that promote equity of access particularly to those with low literacy who are most vulnerable to breathlessness.
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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 | 0.134 | 0.476 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".