Interpreting Laboratory Results with Complementary Health Information: A Human Factors Perspective
Bibliographic record
Abstract
The desire to access personal and high-quality health information electronically is increasing, not only in Canada, but globally. With the advent of the COVID - 19 pandemic the desire and demand for telemedicine and timely access to personal health data such as online laboratory (lab) results has increased substantially. This study examines citizens' perspectives of being provided with high-quality information about a specific lab test (i.e., potassium) in the same display as a trend graph. Therefore, the objective of this study is to test how participants managed this additional information about the context of the test, understood, and applied it. The researchers analyzed the responses of semi-structured interviews with Canadian participants (N=24) using conventional content analysis. This paper examined four themes related to providing complementary information concurrently with lab results in the same display: 1) Benefits of Collocated Information, 2) Information Overload, 3) Misinterpretation, 4) Confusion. This study provided examples of some of the difficulties that the participants faced accessing their lab values online, while navigating and discerning complimentary high-quality health information available in their patient portal.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".