Quality improvement collaborative approach to COVID-19 pandemic preparedness in long-term care homes: a mixed-methods implementation study
Bibliographic record
Abstract
BACKGROUND: The devastating impact of the COVID-19 pandemic on long-term care (LTC) homes underscores the importance of effective pandemic preparedness and response. This mixed-methods, implementation science study investigated how a virtual-based quality improvement (QI) collaborative approach can improve uptake of pandemic-related promising practices and shared learning across six LTC homes in British Columbia, Canada in 2021 during the COVID-19 pandemic health emergency. METHODS: QI teams consisting of residents, family/informal caregivers, care providers and leadership in LTC homes are supported by QI facilitation and shared learning through virtual communication platforms. QI projects address gaps in outbreak preparation, prevention and response; planning for care; staffing; and family presence. Thematically analysed semi-structured qualitative interviews and a validated questionnaire on organisational readiness investigated participants' perceptions of challenges, success factors and benefits of participating in the virtual QI collaborative approach. RESULTS: Nine themes were identified through interview analysis, including two related to challenges (ie, making time for QI and hands tied by external forces), four regarding factors for successes (ie, team buy-in, working together as a team, bringing together diverse perspectives and facilitators keep us on track) and three on the benefits of the QI collaborative approach (ie, seeing improvements, staff empowerment and appetite for change). Continuous QI facilitation and coaching for QI teams was feasible and sustainable virtually via video conferencing (Zoom). The QI team members showed limited engagement on the virtual communication platform (Slack), which was predominantly used by the implementation science team and QI facilitators to coordinate the study and QI projects, respectively. CONCLUSIONS: The virtual-based QI collaborative approach to pandemic preparedness supported LTC homes to rapidly and successfully form multidisciplinary QI teams, learn about QI methods and conduct timely QI projects to implement promising practice for improved COVID-19 pandemic response.
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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 | 0.106 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".