Information quality and dynamics of patients' interactions on tonsillectomy web resources
Bibliographic record
Abstract
Information technologies have drastically altered the way patients gather health-related information. By analysing web resources on tonsillectomy, we expose information quality and dynamics of patients' interactions in the online continuum. Readability was assessed using Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning Fog Index (GFI). Comprehensibility and actionability were assessed using the Patient Education Materials Assessment Tool (PEMAT). Metrics of forums included author characteristics (level of disclosure, gender, age, avatar image, etc.), posts' motive (community support vs. medical information) and content (word count, emoticon use, number of replies, etc.). Analysis of 6 professional medical websites, of 10 health information portals, and of 3 discussion forums totalizing 1369 posts on 358 threads, from January 1, 2007 to December 31, 2014, reveals that online resources exceed understandability recommendations. Women were more present on online health forums (68.2% of authors disclosing their gender) and invested themselves more in their avatar. Authors replying were significantly older than authors of original posts (39.7 ± 0.8 years vs. 29.2 ± 0.9 years, p < 0.001). The degree of self-disclosure was inversely proportional to the requests for medical information (p < 0.001). Men and women were equally seeking medical information (men: 74.0%, women: 77.0%) and community support (men: 65.7%, women: 70.4%), however women responded more supportively (women 86.2%, men 59.1%, p < 0.001). The dynamics of patients' interactions used to overcome accessibility difficulties encountered is complex. This work outlines the necessity for comprehensible medical information to adequately answer patients' needs.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".