Health care and social media: What patients really understand
Bibliographic record
Abstract
<ns4:p> <ns4:italic>Background</ns4:italic> : Low health literacy is associated with decreased patient compliance and worse outcomes - with clinicians increasingly relying on printed materials to lower such risks. Yet, many of these documents exceed recommended comprehension levels. Furthermore, patients look increasingly to social media (SoMe) to answer healthcare questions. The character limits built into Twitter encourage users to publish small quantities of text, which are more accessible to patients with low health literacy. The present authors hypothesize that SoMe posts are written at lower grade levels than traditional medical sources, improving patient health literacy. <ns4:italic>Methods:</ns4:italic> The data sample consisted of the first 100 original tweets from three trending medical hashtags, leading to a total of 300 tweets. The Flesch-Kincaid Readability Formula (FKRF) was used to derive grade level of the tweets. Data was analyzed via descriptive and inferential statistics. <ns4:italic>Results:</ns4:italic> The readability scores for the data sample had a mean grade level of 9.45. A notable 47.6% of tweets were above ninth grade reading level. An independent-sample t-test comparing FKRF mean scores of different hashtags found differences between the means of the following: #hearthealth versus #diabetes (t = 3.15, p = 0.002); #hearthealth versus #migraine (t = 0.09, p = 0.9); and #diabetes versus #migraine (t = 3.4, p = 0.001). <ns4:italic>Conclusions:</ns4:italic> Tweets from this data sample were written at a mean grade level of 9.45, signifying a level between the ninth and tenth grades. This is higher than desired, yet still better than traditional sources, which have been previously analyzed. Ultimately, those responsible for health care SoMe posts must continue to improve efforts to reach the recommended reading level (between the sixth and eighth grade), so as to ensure optimal comprehension of patients. </ns4:p>
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".