Bibliographic record
Abstract
In the health care system, common elements of SEMs include pooled referrals and waiting lists, centralized intake through a single-entry point, and triage for urgency and appropriateness. Four programs including the use of SEMs from the Canadian context were examined: the Winnipeg Central Intake System for hip and knee replacements, the British Columbia Surgical Strategy, the Nova Scotia Hip and Knee Action Plan, and the Saskatchewan Surgical Initiative. SEMs were generally implemented as 1 element of broader strategies to reduce surgical waits and enhance quality and safety of surgical services.Key success factors for implementation of SEMs included: Concurrent investments in surgical capacity and health system resources. The establishment of standardized clinical pathways to reduce care variation. Strong leadership, including a focus on change management and use of clinician champions. Standardized data collection and public reporting on key performance indicators. Concurrent focus on quality improvement and patient-centred care. Challenges for implementation of SEMs included: Effectively managing change and resistance to change. Challenges in other areas of the health system that could impact wait times. Maintaining strategic focus and predictable funding, especially in the face of external shocks.
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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.107 | 0.019 |
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".