Development, characteristics and impact of quality improvement casebooks: a scoping review
Bibliographic record
Abstract
BACKGROUND: Quality improvement (QI) casebooks, compilations of QI experiences, are one way to share experiential knowledge that healthcare policy-makers, managers and professionals can adapt to their own contexts. However, QI casebook use, characteristics and impact are unknown. We aimed to synthesize published research on QI prevalence, development, characteristics and impact. METHODS: We conducted a scoping review by searching MEDLINE, EMBASE, CINAHL and SCOPUS from inception to 4 February 2021. We extracted data on study characteristics and casebook definitions, development, characteristics (based on the WIDER [Workgroup for Intervention Development and Evaluation Research] framework) and impact. We reported findings using summary statistics, text and tables. RESULTS: We screened 2999 unique items and included five articles published in Canada from 2011 to 2020 describing three studies. Casebooks focused on promoting positive weight-related conversations with children and parents, coordinating primary care-specialist cancer management, and showcasing QI strategies for cancer management. All defined casebooks similarly described real-world experiences of developing and implementing QI strategies that others could learn from, emulate or adapt. In all studies, casebook development was a multistep, iterative, interdisciplinary process that engages stakeholders in identifying, creating and reviewing content. While casebooks differed in QI topic, level of application and scope, cases featured common elements: setting or context, QI strategy details, impacts achieved, and additional tips for implementing strategies. Cases were described with a blend of text, graphics and tools. One study evaluated casebook impact, and found that it enhanced self-efficacy and use of techniques to improve clinical care. Although details about casebook development and characteristics were sparse, we created a template of casebook characteristics, which others can use as the basis for developing or evaluating casebooks. CONCLUSION: Future research is needed to optimize methods for developing casebooks and to evaluate their impact. One approach is to assess how the many QI casebooks available online were developed. Casebooks should be evaluated alone or in combination with other interventions that support QI on a range of outcomes.
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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 | 0.131 | 0.416 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.059 | 0.057 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".