Implementation and Evaluation of Innovation in Integrated Care: Strategies to Overcome the Challenge of Work as Imagined
Bibliographic record
Abstract
Health system learning is an iterative process. The six phases of a learning health system (LHS) are problem identification, design, implementation, evaluation, modification, and dissemination. In theory, evaluation is supposed to inform design and implementation and in turn implementation process and modification as the direct result of the evaluation endeavour set the direction for future evaluation. In other words, evaluation and implementation go hand-in-hand. In practice, however, when designing an integrated model of care, more often than not in the fast-paced environment of the healthcare where lives are literally at stake, both evaluation and implementation planning are sacrificed. This happens for many reasons, from lack of resources to lack of expertise. One main reason that work as imagined is perceived as work as done is that those who run the program are smart and dedicated people who are well aware of the needs of their communities and clients, and have their best interest at heart. East Toronto Health Partners (ETHP) has invested in evaluations since 2018 to create a learning health system by embedding rapid cycles of evaluation to support learning, knowledge transfer, and decision making for scale and spread of their new models of care. One of the main findings of our evaluation work was that implementation planning and monitoring the implementation process raised the likelihood of a successful evaluation which in turn resulted in tangible improvement in the program. In our poster presentation, we will focus on both implementation and evaluation. We showcase the link between the logic model/evaluation plan and the implementation plan. We will cover the following questions: Why having an implementation plan is necessary to bring an idea for an intervention to a reality? How an implementation plan helps you to carry out your evaluation plan more successfully? What questions you should be thinking about when you are developing your implementation plan? We will share examples of best practices. The poster will also provide practical implementation templates that can be used in different setting.
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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 | 0.539 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.045 | 0.040 |
| Open science | 0.009 | 0.036 |
| Research integrity | 0.011 | 0.012 |
| 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".