Case study of evaluations that go beyond clinical outcomes to assess quality improvement diabetes programmes using the <scp>D</scp>iabetes <scp>E</scp>valuation <scp>F</scp>ramework for <scp>I</scp>nnovative <scp>N</scp>ational <scp>E</scp>valuations (<scp>DEFINE</scp>)
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Investments in efforts to reduce the burden of diabetes on patients and health care are critical; however, more evaluation is needed to provide evidence that informs and supports future policies and programmes. The newly developed Diabetes Evaluation Framework for Innovative National Evaluations (DEFINE) incorporates the theoretical concepts needed to facilitate the capture of critical information to guide investments, policy and programmatic decision making. The aim of the study is to assess the applicability and value of DEFINE in comprehensive real-world evaluation. METHOD: Using a critical and positivist approach, this intrinsic and collective case study retrospectively examines two naturalistic evaluations to demonstrate how DEFINE could be used when conducting real-world comprehensive evaluations in health care settings. RESULTS: The variability between the cases and the evaluation designs are described and aligned to the DEFINE goals, steps and sub-steps. The majority of the theoretical steps of DEFINE were exemplified in both cases, although limited for knowledge translation efforts. Application of DEFINE to evaluate diverse programmes that target various chronic diseases is needed to further test the inclusivity and built-in flexibility of DEFINE and its role in encouraging more comprehensive knowledge translation. CONCLUSIONS: This case study shows how DEFINE could be used to structure or guide comprehensive evaluations of programmes and initiatives implemented in health care settings and support scale-up of successful innovations. Future use of the framework will continue to strengthen its value in guiding programme evaluation and informing health policy to reduce the burden of diabetes and other chronic diseases.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".