Addressing Health Literacy in Patient Decision Aids: An Update from the International Patient Decision Aid Standards
Bibliographic record
Abstract
Background There is increasing recognition of the importance of addressing health literacy in patient decision aid (PtDA) development. Purpose An updated review as part of IPDAS 2.0 examined the extent to which PtDAs are designed to meet the needs of people with low health literacy/socially-disadvantaged populations. Data Sources Reference lists of Cochrane reviews of randomized controlled trials (RCTs) of PtDAs (2014, 2017, and upcoming 2021 versions). Study Selection RCTs that assessed the impact of PtDAs on low health literacy or other socially-disadvantaged groups (i.e., ≥50% participants from socially-disadvantaged groups and/or subgroup analysis in socially-disadvantaged group/s). Data Extraction Two researchers independently extracted data into a standardized form including PtDA development and evaluation details. We searched online repositories and emailed authors to access PtDAs to verify grade reading level, understandability, and actionability. Data Synthesis Twenty-five of 213 RCTs met the inclusion criteria, illustrating that only 12% of studies addressed the needs of low health literacy or other socially-disadvantaged groups. Grade reading level was calculated in 8 of 25 studies (33%), which is recommended in previous IPDAS guidelines. We accessed and independently assessed 11 PtDAs. None were written at sixth-grade level or below. Ten PtDAs met the recommended threshold for understandability, but only 5 met the recommended threshold for actionability. We also conducted a post hoc subgroup meta-analysis and found that knowledge improvements after receiving a PtDA were greater in studies that reported using strategies to reduce cognitive demand in PtDA development compared with studies that did not (χ 2 = 14.11, P = 0.0002, I 2 = 92.9%). Limitations We were unable to access 13 of 24 PtDAs. Conclusions. Greater attention to health literacy and socially-disadvantaged populations is needed in the field of PtDAs to ensure equity in decision support.
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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 | 0.218 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".