False Calm and False Alarm: A Qualitative Study of Confusion and Misinterpretation of a Laboratory Results Graph
Bibliographic record
Abstract
Health consumers (i.e., citizens), increasingly review their personal health information on patient portals. However, health information, especially graphs, can be difficult to interpret and use. To investigate reporting attributes that confused participants, what they misinterpreted, and their follow-up intentions, we examined participants' (N = 24) think-aloud responses to a laboratory system graph depicting lab values over time. Overall, 18 participants expressed confusion at least once and 12 misinterpreted one or more aspects of the graph. Common areas of confusion included axis labelling and the significance of value changes. Confusion led to misinterpretation (e.g., incorrect date, more than two results depicted, change was not important). The findings underscore the need for improved graph design. To enhance user performance with laboratory graphs, we recommend implementing generic design principles for graphs and exploring potential strategies unique to this context (e.g., standardized Y axes values, personalized reference ranges, inclusion of explanatory text). This research emphasizes the importance and value of user-centered design for improving health information for citizens.
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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 | 0.057 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".