Bibliographic record
Abstract
Improving end of life care requires better population level data Although the oldest health statistics are based on death certificates, one of the weakest areas of health information is how we die. For example, in Canada 220 000 deaths occur each year. We know how many people died, and whether it was from cancer, heart disease, or other causes. But we have no idea how many of these people died in pain, hooked up to life support they didn't want, or alone. In the absence of systematic information and monitoring of end of life care and comparisons across health regions (or health care organisations) there is no possibility of learning what is possible (those regions with the highest ratings), nor of tracking whether improvements are occurring. Twenty years ago, the challenge was to engage healthcare workers in the care of the dying. Ten years ago, the challenge was to engage healthcare organisations in quality improvement efforts on end of life care. Today, the challenge is to develop systematic and comprehensive information on the quality of end of life care at the population level. Canada, like many countries, has a well developed health information structure—organisations such as Statistics Canada and the …
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.020 | 0.038 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".