Complexities of a Provincial CIS Implementation: Thinking Beyond Scale
Bibliographic record
Abstract
CIS implementations are complex processes involving numerous teams, planning changes to both business and technical processes, and extensive change management. The complexity of implementation increases exponentially when dealing with implementation across an entire province rather than just a single site implementation. This paper addresses the One Person One Record Program in Nova Scotia, Canada where a single CIS will be implemented across the entire province involving 47 acute care facilities and 1400 individual ambulatory clinics. Developing and delivering localized role-specific training to end users is directly affected by the extensive arrange of unique user roles and is part of the complexity in this transformation program. Challenges arising from the additional complexity will be shared as well as lessons learned to support the implementations of future leaders with plans to lead such transformations in their own regions.
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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 | 0.031 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".