Exploring the Impact of Evaluation on Learning and Health Innovation Sustainability: Protocol for a Realist Synthesis
Bibliographic record
Abstract
Abstract Background Within the Learning Health System (LHS) model, learning routines, including evaluation, allow for continuous incremental change to take place. Within these learning routines, evaluation assists in problem identification, data collection, and data transformation into contextualized information, which is then re-applied to the LHS environment. Evaluation that catalyzes learning and improvement may also contribute to health innovation sustainability. However, there is little consensus as to why certain evaluations seem to support learning and sustainability, while others impede it. This realist synthesis seeks to understand the contextual factors and underlying mechanisms or drivers that best support health systems learning and sustainable innovation. Methods This synthesis will be guided by Pawson and colleagues’ 2005 and Emmel and colleagues’ 2018 guidelines for conducting realist syntheses. The review process will encompass five steps: 1. Scoping the Review, 2. Building Theories, 3. Identifying the Evidence, 4. Evidence Selection and Appraisal, and 5. Data Extraction and Synthesis. An Expert Committee comprised of leaders in evaluation, innovation, sustainability, and realist methodology will guide this synthesis. Review findings will be reported using the RAMESES guidelines. Discussion The use of a realist review will allow for exploration and theorizing about the contextual factors and underlying mechanisms that make evaluations ‘work’ (or ‘not work’) to support learning and sustainability. Depending on results, we will attempt to synthesize findings into a series of recommendations for evaluations with the intention to support health systems learning and sustainability. Finalized results will be presented at national and international conferences, as well as disseminated via a peer-reviewed publication. Systematic review registration : This realist synthesis protocol has been registered with PROSPERO (https://www.crd.york.ac.uk/prospero/ ID 382690)
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.259 | 0.408 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.104 | 0.021 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".