Is There Value Without Context? A Survey Evaluating How Laboratory Test Results Are Presented to Patients in Canada
Bibliographic record
Abstract
Although direct reporting of laboratory test results to patients improves engagement and utilization of healthcare services, this assumes that results are presented in a manner that can be comprehended and acted upon by patients. To evaluate the practice of patient reporting across Canadian laboratories, a voluntary survey was distributed. 22 responses were received from laboratories and laboratory networks nationwide, representative of the range of Canadian laboratory and patient demographics. Despite the Connected Care for Canadians Act being passed in June 2024, one-third of respondents do not provide results to patients. Of those that do, results largely replicate physician reports and are heterogeneous between labs, with different strategies used to present data and flag abnormalities. A minority of labs suppress some testing from patient receipt, modify reports to improve patient comprehension, or provide graphs to support interpretation and trending. Laboratory professionals largely agreed that there are benefits in modifying reports to aid in patient comprehension but expressed concern that patient health literacy is currently in adequate. This may lead to increased anxiety, misinterpretation of results, follow-up questions, self-diagnosis, and undue stress until a healthcare provider could be consulted. Collaboration with patients and healthcare providers is necessary to develop guidelines on meaningful direct-to-patient reporting.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".