Scoping review study to identify how communities in the USA, Australia, New Zealand and Canada use quality improvement (QI) approaches to address community health and well-being
Bibliographic record
Abstract
INTRODUCTION: Both US and global communities lag on key health indicators. There has been limited progress in building capacity to improve health beyond the healthcare field. Yet, communities also need to engage in health improvement initiatives. A substantial body of literature describes standards and core components for quality improvement (QI) approaches in clinical settings. This study aims to determine how communities in the USA, Australia, New Zealand and Canada use QI approaches for health and well-being improvement and how such approaches compare to those in clinical settings. METHODS AND ANALYSIS: ) and other published protocols. We developed research questions in an iterative process and used the Population, Intervention, Comparison, Outcomes strategy to determine eligibility criteria. Electronic databases deemed appropriate (Web of Science, Scopus, and Proquest Health Management) will be searched for studies that meet inclusion criteria. References of included studies will be included when relevant. Two reviewers will independently screen all abstracts and full-text studies for inclusion. A third reviewer will adjudicate disagreements that arise. An instrument will be developed to extract data from included studies. Quantitative and qualitative results will be reported. ETHICS AND DISSEMINATION: We developed this protocol to systematically conduct a scoping review of how US communities use QI approaches to address community health and well-being. Results will benefit multiple stakeholders by informing how to better support, design and evaluate community well-being improvement interventions. Results will be distributed through peer-reviewed journals, conferences, presentations and a public health graduate course.
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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 | 0.158 | 0.208 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".