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Record W3197401797 · doi:10.1186/s12961-021-00777-z

Development, characteristics and impact of quality improvement casebooks: a scoping review

2021· review· en· W3197401797 on OpenAlexaffabout
Natalie N. Anderson, Anna R. Gagliardi

Bibliographic record

VenueHealth Research Policy and Systems · 2021
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsCasebookCINAHLHealth administrationContext (archaeology)Medical educationMedicineHealth careHealth services researchQuality managementPsychologyNursingPublic healthPolitical scienceBusinessPsychological interventionMarketing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.054
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.956
GPT teacher head0.823
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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