Addressing the ethical problem of underdiagnosis in the post-pandemic Canadian healthcare system
Bibliographic record
Abstract
Proper diagnosis is essential for effective treatment, yet in Canada health conditions are commonly underdiagnosed at all levels of the health system, meaning that they go undiagnosed or are diagnosed only after a delay. Underdiagnosis leads to inadequate treatment and potentially insufficient recovery and rehabilitation, as well as costly inefficiencies, such as repeat medical visits. Moreover, disparities in underdiagnosis in which vulnerable groups, such as women and Indigenous persons, are properly diagnosed at lower rates worsen existing inequities, which threatens the overall health of the general population. As health leaders and policy-makers seek to strengthen Canada's strained healthcare system, it will be important to address underdiagnosis and its causes, including systematic bias. Providing timely and accurate diagnoses for all patients is an essential component of delivering high quality, efficient, ethical, and cost-effective healthcare. The Canadian College of Health Leaders' Code of Ethics offers a framework for addressing underdiagnosis equitably. Utilizing the framework, suggestions are made for actions that can be taken at all levels of the health system to reduce underdiagnosis.
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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 | 0.045 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.051 | 0.049 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".