Implementing a nurse-led quality improvement project in nursing home during COVID 19 pandemic: A qualitative study
Bibliographic record
Abstract
Background: There is broad consensus that the quality of nursing home (NH) care is a research priority to advance NH practice. However, NHs often fail to implement quality improvement (QI) research projects and complex circumstances such as Coronavirus disease 19 (COVID-19) pandemic may further hinder compliance. This study aims to describe the challenges associated with implementing a nurse-led QI project in NH during COVID-19 pandemic and potential strategies for their overcoming. Methods: A descriptive qualitative study was performed, and three data collection strategies employed, including: 1. semi-structured, open-ended interviews with follow-up questions (one NH manager, three members of the NH staff, and two family caregivers of people with advanced dementia); 2. research diary; and 3. in-the-field-notes. A combined deductive and inductive content analysis was adopted to analyze data. Results: Challenges may be anticipated or unanticipated. QI projects should include preliminary assessments to identify the willingness to change and establish partnerships at multiple levels with all stakeholders, adjust the implementation plan to the organizational context, and be open to ongoing changes. Conclusions: Early and regular engagement of stakeholders strengthen relationships. Moreover, an ongoing reflective practice throughout the entire implementation process promotes openness to change, and finally learning and improvement.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".