Accessible health care I commend Meridith Marks
Bibliographic record
Abstract
to the problems faced by Canadians with disabilities in accessing the health care system.1 However, I believe the most fundamental threat to the well-being of these patients resides in the potential for expansion of the pri-vate health care insurance industry in this country. By stipulating that health care must be accessible, universal and publicly administered, the Canada Health Act de facto ensures that people with dis-abilities are not denied health care coverage or do not have their coverage loaded (i.e., higher premiums to re-flect greater actuarial risk). Although health care funding in Canada is not calculated actuarially, its costs are shared by all Canadians through their taxes. Private insurance companies oper-ate on a for-profit basis. They employ actuarial methods to screen applicants for conditions that represent an insur-ance risk. People with disabilities or other pre-existing medical conditions who applied for private coverage would therefore face higher, perhaps unaf-fordable, premiums or would be de-nied coverage altogether. The publicly funded health care system would have an uncertain future in a 2-tiered sce-nario, but people with disabilities and chronic conditions would be com-pletely dependent on it. I believe that guaranteed and afford-able insurance is the cornerstone of health care access for patients with dis-abilities and chronic conditions. All Canadian physicians should work to ensure that such insurance is not jeop-ardized.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".