Bibliographic record
Abstract
Canadians face prolonged waits for primary care, specialist care, hospital care, elective surgery, and advanced imaging relative to peer countries. A root problem is unclear queue management expectations. If programs have no mandate to provide timely care, the intuitive approach to demand challenges is not to innovate and improve, but to block access, create a queue, and force patients elsewhere. Patient care accountability frameworks define program expectations and accountability zones, clarifying that every patient has an accountable healthcare home and every program has a population (accountability zone). Program accountabilities include timely patient assessment and disposition; budget, space, and nursing care for program patients; and contingency plans for surges and queues. Accountability frameworks are an evolutionary stressor that would drive strategies to expedite appropriate care in the right place, to move patients out of queues into care. This article discusses accountability, accountability frameworks, and accountability strategies to improve system-wide access.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Theoretical or conceptual | high |
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 | 0.104 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.063 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".