Quality care metrics (QC-M) in nursing and midwifery care processes: a rapid realist review (RRR) protocol
Bibliographic record
Abstract
Background: In 2018, the Office of the Nursing and Midwifery Services Director (ONMSD) completed phase one of work which culminated in the development and launch of seven research reports with defined suites of quality care process metrics (QC-Ms) and respective indicators for the practice areas – acute care, midwifery, children’s, public health nursing, older persons, mental health and intellectual disability nursing in Ireland. This paper presents a rapid realist review protocol that will systematically review the literature that examines QC-M in practice; what worked, or did not work for whom, in what contexts, to what extent, how and why? Methods : The review will explore if there are benefits of using the QC-Ms and what are the contexts in which these mechanisms are triggered. The essence of this rapid realist review is to ascertain how a change in context generates a particular mechanism that produces specific outcomes. A number of steps will occur including locating existing theories on implementation of quality care metrics, searching the evidence, selecting relevant documents, data extraction, validation of findings, synthesising and refining programme theory. This strategy may help to describe potential consequences resulting from changes in context and their interactions with mechanisms. Initial theories will be refined throughout the process by the local reference panel, comprised of eight key intervention stakeholders, knowledge users such as healthcare professionals and an expert panel. Ethical approval is not required for this rapid realist review. Conclusion: It is anticipated that the final programme theory will help to explain how QC-Ms work in practice; for whom, why and in what circumstances. Findings of this review could help to give insights into realism as a framework and how nursing and midwifery QC-Ms have been implemented previously.
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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 | 0.254 | 0.409 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.109 | 0.033 |
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".