Examining adaptive models of care implemented in hospital ICUs during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The emergence of the COVID-19 pandemic led to an increased demand for hospital beds, which in turn led to unique changes to both the organisation and delivery of patient care, including the adoption of adaptive models of care. Our objective was to understand staff perspectives on adaptive models of care employed in intensive care units (ICUs) during the pandemic. METHODS: We interviewed 77 participants representing direct care staff (registered nurses) and members of the nursing management team (nurse managers, clinical educators and nurse practitioners) from 12 different ICUs. Thematic analysis was used to code and analyse the data. RESULTS: Our findings highlight effective elements of adaptive models of care, including appreciation for redeployed staff, organising aspects of team-based models and ICU culture. Challenges experienced with the pandemic models of care were heightened workload, the influence of experience, the disparity between model and practice and missed care. Finally, debriefing, advanced planning and preparation, the redeployment process and management support and communication were important areas to consider in implementing future adaptive care models. CONCLUSION: The implementation of adaptive models of care in ICUs during the COVID-19 pandemic provided a rapid solution for staffing during the surge in critical care patients. Findings from this study highlight some of the challenges of implementing redeployment as a staffing strategy, including how role clarity and accountability can influence the adoption of care delivery models, lead to workarounds and contribute to adverse patient and nurse outcomes.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".