Evaluating investment in quality improvement capacity building: a systematic review
Bibliographic record
Abstract
PURPOSE: Leading health systems have invested in substantial quality improvement (QI) capacity building, but little is known about the aggregate effect of these investments at the health system level. We conducted a systematic review to identify key steps and elements that should be considered for system-level evaluations of investment in QI capacity building. METHODS: We searched for evaluations of QI capacity building and evaluations of QI training programmes. We included the most relevant indexed databases in the field and a strategic search of the grey literature. The latter included direct electronic scanning of 85 relevant government and institutional websites internationally. Data were extracted regarding evaluation design and common assessment themes and components. RESULTS: 48 articles met the inclusion criteria. 46 articles described initiative-level non-economic evaluations of QI capacity building/training, while 2 studies included economic evaluations of QI capacity building/training, also at the initiative level. No system-level QI capacity building/training evaluations were found. We identified 17 evaluation components that fit within 5 overarching dimensions (characteristics of QI training; characteristics of QI activity; individual capacity; organisational capacity and impact) that should be considered in evaluations of QI capacity building. 8 key steps in return-on-investment (ROI) assessments in QI capacity building were identified: (1) planning-stakeholder perspective; (2) planning-temporal perspective; (3) identifying costs; (4) identifying benefits; (5) identifying intangible benefits that will not be included in the ROI estimation; (6) discerning attribution; (7) ROI calculations; (8) sensitivity analysis. CONCLUSIONS: , can be used to start closing this knowledge gap.
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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 | 0.061 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".