© 2006 CMA Media Inc. or its licensors Commentary Public Health
Bibliographic record
Abstract
As a Canadian member of the US Institute of Medicine(IOM) Committee on Health Literacy,1 I was im-pressed with the extent to which individual phy-sicians and medical institutes have led the way in putting the issue of health literacy * on professional and political agen-das in the United States. Not only did individual physicians undertake considerable research on health literacy, but the American Medical Association (AMA) established an Ad Hoc Committee on Health Literacy, which produced an influential report2 that contributed to the identification of health literacy as a health goal for the United States and to the establishment of the IOM Committee on Health Literacy and the support of health literacy initiatives through the AMA Foundation. In striking contrast, the documented contribution of indi-vidual physicians and organized medicine in Canada to the field of health literacy has been limited. Little research has been conducted on health literacy by Canadian physicians,
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.045 | 0.021 |
| Insufficient payload (model declined to judge) | 0.094 | 0.045 |
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".