Health Literacy Among Surgical Patients: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Abstract Health literacy is the extent to which patients are able to understand and act upon health information. This concept is important for surgeons as their patients have to comprehend the nature, risks and benefits of surgical procedures, adhere to perioperative instructions, and make complex care decisions about interventions. Our review aimed to determine the prevalence of limited health literacy of the surgical patient population. A search of MEDLINE and EMBASE was performed from inception until January 14th 2017 for experimental and observational studies reporting surgical patients’ health literacy measurement. Overall pooled proportion of surgical patients with limited health literacy was calculated using a random‐effects model and methodologic quality was assessed. A total of 40 studies representing 18,895 surgical patients were included in our quantitative synthesis. Pooled estimate of limited health literacy was 31.7% (95%CI 24.7–39.2%, I2 99.0%). There was low risk of bias among the majority of the 51 studies included in the qualitative synthesis. Statistical heterogeneity could not be fully accounted for by methodologic quality or patient and surgical characteristics. However, some of the heterogeneity was accounted by measurement tool [combined proportions with the REALM and NVS of 35.6 (95%CI 31.5–39.9, I2 73.0%)]. A number of different health literacy measurement tools were used (19 overall). Our review demonstrates a high prevalence of limited health literacy among surgical patients with considerable heterogeneity. Our findings suggest the importance of recognizing and addressing surgical patients with limited health literacy and the need for standardization in measurement tools.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".