Managing Strategy to Enhance Care for Children
Bibliographic record
Abstract
The adoption of the Balanced Scorecard philosophy of measure, monitor and manage by The Hospital for Sick Children (SickKids) has resulted in SickKids' staff understanding, appreciating and ultimately being able to accept the enhanced transparency and accountability around performance, at both the system and hospital levels. The leadership of the organization observed these differences after initial SickKids scorecard update meetings, realizing this was not a flavour of the month but a totally new way of operating in a quest to achieve SickKids' vision and mission. Almost immediately, the internal culture began to shift as staff better understood how their roles actively contribute to the organization's ability to execute on its strategy. Based on 2010 staff engagement results, 70% of staff "see a direct link between personal work objectives and SickKids' strategy," while 60% were familiar with the newly released strategic plan, unprecedented results based on current benchmarks. This article provides an overview of the SickKids strategy management system, outlining both best practices and the journey from its launch to induction into the Balanced Scorecard Hall of Fame. Performance, at all levels across the enterprise, has shown measureable improvement with the introduction of the comprehensive strategy management system.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".