Development of a Quality Improvement and Patient Safety Research Conceptual Framework to Inform Research Priorities for the Irish Healthcare System: Protocol for a Scoping Review
Bibliographic record
Abstract
Background A core part of the remit of the Evidence Based QUality Improvement and Patient Safety (EQUIPS) Research Network (2024–2029) is to inform research priorities for the Irish healthcare system. Previously, research prioritisation exercises have treated quality improvement (QI) and patient safety research as separate fields. Given the growth of QI research, an integrated approach is required to generate a full view of the continuum of knowledge generation and translation activities in quality and safety contexts. The co-creation of a Quality Improvement and Patient Safety Research Conceptual Framework among researchers, knowledge users and patient groups is required to outline important topics, research questions and methodologies, applicable to all areas of healthcare. A scoping review of the literature is required to examine existing QI and patient safety research frameworks to inform the development of a harmonised conceptual framework for the Irish context. Methods The scoping review will follow JBI guidelines for developing and co-creating scoping review protocols with knowledge users. The completed review will be reported according to the PRISMA extension for Scoping Reviews, and Guidance for Reporting Involvement of Patients and the Public 2 (GRIPP2) checklist which supports enhanced reporting of patient and public involvement in research. We will search the following databases from 2000–2025 focussing on literature sources from developed countries: Ovid MEDLINE, Embase, CINAHL, Scopus, Cochrane Library, Web of Science. We will map included papers using an extraction template developed to fulfil the study aims. Two reviewers will independently screen studies, and discrepancies will be addressed by consensus using a third reviewer. Dissemination Findings will be used to inform the development of a meta-framework that will be used to guide QI and patient safety research priority setting, measurement and monitoring, implementation, and knowledge translation activities in Ireland, at local and national levels. OSF https://doi.org/10.17605/OSF.IO/FGWUA
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 | 0.205 | 0.217 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.082 | 0.020 |
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".