Bibliographic record
Abstract
A n abyss divides common understandings aboutwaiting lists from evidence about their nature andcauses and what might work to rationalize them.1 In a recent comprehensive report for Health Canada2 we found that the state of waiting-list information and man-agement systems in Canada is woefully inadequate, particu-larly for elective procedures. Here, we identify key lessons and outline a number of initiatives that should contribute to more durable solutions both in Canada and in other countries experiencing similar problems. Fairness: a core public expectation Why should we worry about how waiting lists — espe-cially those for elective procedures — are organized and managed? The main reason is fairness or equity. A core underpinning of publicly financed health care systems is “to each according to his or her need. ” Assuming that a health care intervention offers a reasonable probability of tangible
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.058 | 0.041 |
| Insufficient payload (model declined to judge) | 0.070 | 0.031 |
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".