A scoping review of the use of quality improvement methods by community organizations in the United States, Australia, New Zealand, and Canada to improve health and well-being in community settings
Bibliographic record
Abstract
Abstract Background Health-care facilities have used quality improvement (QI) methods extensively to improve quality of care. However, addressing complex public health issues such as coronavirus disease 2019 and their underlying structural determinants requires community-level innovations beyond health care. Building community organizations’ capacity to use QI methods is a promising approach to improving community health and well-being. Objectives We explore how community health improvement has been defined in the literature, the extent to which community organizations have knowledge and skill in QI and how communities have used QI to drive community-level improvements. Methods Per a published study protocol, we searched Scopus, Web of Science, and Proquest Health management for articles between 2000 and 2019 from USA, Australia, New Zealand, and Canada. We included articles describing any QI intervention in a community setting to improve community well-being. We screened, extracted, and synthesized data. We performed a quantitative tabulation and a thematic analysis to summarize results. Results Thirty-two articles met inclusion criteria, with 31 set in the USA. QI approaches at the community level were the same as those used in clinical settings, and many involved multifaceted interventions targeting chronic disease management or health promotion, especially among minority and low-income communities. There was little discussion on how well these methods worked in community settings or whether they required adaptations for use by community organizations. Moreover, decision-making authority over project design and implementation was typically vested in organizations outside the community and did not contribute to strengthening the capability of community organizations to undertake QI independently. Conclusion Most QI initiatives undertaken in communities are extensions of projects in health-care settings and are not led by community residents. There is urgent need for additional research on whether community organizations can use these methods independently to tackle complex public health problems that extend beyond health-care quality.
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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 | 0.056 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.048 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".