Appropriateness of Language Used in Patient Educational Materials from 24 National Anesthesiology Associations
Bibliographic record
Abstract
BACKGROUND: Patient education materials produced by national anesthesiology associations could be used to facilitate patient informed consent and promote the discipline of anesthesiology. To achieve these goals, materials must use language that most adults can understand. Health organizations recommend that materials be written at the grade 8 level or less to ensure that they are understood by laypersons. The authors, therefore, investigated the language of educational materials produced by anesthesiology associations. METHODS: Educational materials were downloaded from the Web sites of 24 national anesthesiology associations, as available. Materials were divided into eight topics, resulting in 112 separate passages. Linguistic measures were calculated using Coh-Metrix (version 3.0; Memphis, USA) linguistic software. The authors compared the measures to a grade 8 standard and examined the influence of both passage topic and country of origin using multivariate ANOVA. RESULTS: The authors found that 67% of associations provided online educational materials. None of the passages had all linguistic measures at or below the grade 8 level. Linguistic measures were influenced by both passage topic (F = 3.64; P < 0.0001) and country of origin (F = 7.26; P < 0.0001). Contrast showed that passages describing the role of anesthesiologists in perioperative care used language that was especially inappropriate. CONCLUSIONS: Those associations that provided materials used words that were long and abstract. The language used was especially inappropriate for topics that are critical to facilitating patient informed consent and promoting the discipline of anesthesiology. Anesthesiology associations should simplify their materials and should consider screening their materials with linguistic software before making them public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".