A scoping review of online repositories of quality improvement projects, interventions and initiatives in healthcare
Bibliographic record
Abstract
BACKGROUND: Quality improvement (QI) is becoming an important focal point for health systems. There is increasing interest among health system stakeholders to learn from and share experiences on the use of QI methods and approaches in their work. Yet there are few easily accessible, online repositories dedicated to documenting QI activity. METHODS: We conducted a scoping review of publicly available, web-based QI repositories to (i) identify current approaches to sharing information on QI practices; (ii) categorise these approaches based on hosting, scope and size, content acquisition and eligibility, content format and search, and evaluation and engagement characteristics; and (iii) review evaluations of the design, usefulness and impact of their online QI practice repositories. The search strategy consisted of traditional database and grey literature searches, as well as expert consultation, with the ultimate aim of identifying and describing QI repositories of practices undertaken in a healthcare context. RESULTS: We identified 13 QI repositories and found substantial variation across the five categories. The QI repositories used different terminology (eg, practices vs case studies) and approaches to content acquisition, and varied in terms of primary areas of focus. All provided some means for organising content according to categories or themes and most provided at least rudimentary keyword search functionality. Notably, none of the QI repositories included evaluations of their impact. DISCUSSION: With growing interest in sharing and spreading best practices and increasing reliance on QI as a key contributor to health system performance, the role of QI repositories is likely to expand. Designing future QI repositories based on knowledge of the range and type of features available is an important starting point for improving their usefulness and impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.074 | 0.069 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".