Examining Technical Assistance and Its Use in Health System Transformations
Bibliographic record
Abstract
Many health systems are in the midst of transformation. They are slowly moving from the delivery of reactive care focused on individuals to considering proactive ways of supporting the health and well-being of populations. However, the road to what is often called ‘population-health management’ is rife with implementation challenges. One type of implementation support that has been used to navigate these challenges is technical assistance. Though the use of technical assistance is well documented, there is no consensus on a clear definition or understanding of how it can be used to support system transformation. This thesis contributes to the field of technical assistance through three qualitative studies. First, a critical interpretive synthesis develops a definition and logic model for technical assistance. This logic model integrates diverse academic and grey literature. It aims to draw clearer boundaries around technical assistance as a concept and provide a common language for researchers, technical assistance providers, and decision-makers to use. Second, a qualitative descriptive study explores the use of technical assistance in population-health management transformations in England and the U.S., examining what technical assistance has been provided, by whom, and in what areas of application. Finally, a case study unpacks the use of technical assistance for a recent health-system transformation in Ontario. It examines the influence that political factors related to institutions, ideas, interests and external events have on shaping its evolution. Together, these three studies provide greater clarity on the use of technical assistance in health-system transformations and the range of factors that may affect how it is conceptualized and operationalized.
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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 | 0.078 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".