Bibliographic record
Abstract
aint Elizabeth Health Care (SEHC) tells its clients and partner agencies that it is ahead by a century -and not just because it celebrated its 100th anniversary in 2008.Rather, SEHC takes pride in talent management, leadership, innovation, knowledge and experience -precursors to service excellence, client satisfaction and employee engagement.With revenues exceeding $100 million, a designation as a Top 50 Workplace in Canada and status as an undisputed leader in community-based health services, SEHC has a culture of quickly responding to emerging trends and getting it right.President and chief executive officer (CEO) Shirlee Sharkey is no stranger to healthcare's evolution in and devolution to community-based care; her leadership in promoting the imperative and capacity of communities has been equally ahead of its time.Shirlee, the recipient of the University of Toronto's Health Policy, Management and Evaluation (HPME) Leadership Award for 2008 and Ernst and Young's Entrepreneur of the Year award, spoke with Ken Tremblay from St. Elizabeth's head office in Markham, Ontario.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".