Questions & Answers: Words from the Street: Delivering on a Provincial Mandate at the Local Level
Bibliographic record
Abstract
A t the local level, Hospital Project Managers (PMs) were single points of contact for the provincial WTIS project team and were responsible for delivering on all aspects of the WTIS deployment within hospitals.Here, two PMs share their WTIS deployment experience from Beta/Phase I (completed in March 2006) and Phase II (completed in December 2006).It was through valuable insight and feedback such as this that the provincial WTIS project team was able to make improvements for smoother deployment in subsequent phases. IN: How did the WTIS project compare to other healthcare information management/information technology (IM/IT) projects you've been involved in?JC: The WTIS was a focused project with very tight timelines that could not be missed.As one of the Beta hospitals, we had a very strong sense of teamwork because we were playing a big role in providing input for future phases.There was also a sense of excitement because we had the support to bring technology to physician offices that might have been disinclined to move in that direction without this initiative.KFW: Compared to other projects I've been involved in, the WTIS was much more intense and had the added pressure of being implemented within very short timelines.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.173 | 0.060 |
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".