Bibliographic record
Abstract
The recent editorial, “Waiting Lists? What Waiting Lists? Not Nursing’s Problem” (Pringle 2005), challenges nurses to “step up to the plate” with a pithy and solid analysis of the waiting list issue from a nursing perspective and with recommendations for ameliorating it. Although we are not nurses, and are thereby disqualified from stepping up to the plate, we believe that our experience of working for over a year to implement Ontario’s Wait Time Strategy qualifies us to comment on the game. On November 17, 2004, the Minister of Health and Long-Term Care, George Smitherman, officially announced Ontario’s Wait Time Strategy. The strategy is designed to improve access to healthcare services in the public system by December 2006 by reducing the time that adult Ontarians wait for services in five areas: MRI and CT, cancer surgery, cardiac revascularization, cataract surgery and total hip and knee joint replacements. “Wait time” is defined as the time elapsed between the decision to order a scan or surgery and completion of the procedure. Expert panels – comprising clinicians, administrators, researchers and others – are being used to help achieve this strategy. In addition to the five expert service panels, two other panels have focused on efficient surgical practices to increase patient flow and critical care services to support timely access to surgical care. Ongoing input has also been sought from organizations and professional associations including the Registered Nurses’ Association of Ontario, which has provided invaluable advice since the strategy began.
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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.025 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".