Challenges in Evaluating a Prototype Project in a Large Health Authority: Lessons Learned
Bibliographic record
Abstract
Abstract: A developmental evaluation was undertaken to evaluate a prototype test of a new model of perinatal healthcare across acute maternity, public health, and primary care in two hospitals in a large health authority. The project was initiated to bridge gaps in care across the acute and community settings to ensure a seamless perinatal healthcare journey for women. The objective of the evaluation was to support the prototyping process by providing data to inform decisions as the prototype was developed and by documenting decisions as they were made. This article explores challenges faced during the evaluation, including unfamiliarity of the health sector with prototype projects and their inherent uncertainty, a disconnect between the rapid pace of a prototype project and bureaucratic hurdles of working within a large organization, and high leadership turnover throughout the project. How these challenges were addressed, and the lessons learned for future evaluations, are discussed.
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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 | 0.341 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".