Bibliographic record
Abstract
Reports Measuring UpWhile the federal, provincial and territorial governments are making gains on some commitments to renew healthcare, Canadians are not getting the detailed information they need to measure progress in improving healthcare, the Health Council of Canada concluded in its recently released annual report to Canadians.The report tracks the progress governments have made in meeting such commitments as reforming primary healthcare, reducing wait times and health inequalities, modernizing health information systems and improving drug coverage.While there is good news to share, the Health Council emphasized the lack of comparable data and the prevalence of inconsistent or incomplete reporting across the country.First Ministers did not report on comparable health indicators this year, as they had agreed to do, and the federal/provincial/territorial committee that oversees this work has been disbanded.Information about how provinces and territories spend targeted federal funds is not easily accessible, or in some cases, not available at all.Without better data, jurisdictions will fall short of their commitment to more transparent public reporting and greater accountability.www.healthcouncil canada.ca/en/index.php?option=com_content&task=view&id =136&Itemid=115 Emergency Physicians Release Recommendations on Emergency Department OvercrowdingIn response to public concern with delays in emergency departments and the impact of overcrowding and wait times on the health of Canadians, the Canadian Association of Emergency Physicians has issued a new position paper on emergency department overcrowding.The paper includes recommendations to address the immediate situation in Canada's emergency departments, as well as some solutions for the longer-term.For instance, the paper suggests implementing national length-of-stay benchmarks and overcapacity protocols, which would share the responsibility for already admitted hospital patients within all wards of the hospital instead of "warehousing" them in emergency departments.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.277 | 0.110 |
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".