Elucidating insights on how care was prioritized, adapted, and missed during and post pandemic
Bibliographic record
Abstract
Globally, healthcare systems continue to recover and manage system demands, including our sustained HHR pressures exacerbated by the COVID-19 pandemic. Health system leaders need to understand how healthcare was adapted during the pandemic, what contributed to these changes, and the impact of these changes to inform future efforts. The overarching research questions included: What changes to models of care were made during COVID-19 and post-recovery? What factors contributed to changes in models of care? What was the impact of these changes? An exploratory interpretative descriptive qualitative study was undertaken to describe what HHR strategies and changes to models of care delivery were employed during the COVID-19 pandemic and post-pandemic recovery. An inductive thematic analysis was conducted where an investigation team of research staff identified, coded, and categorized prominent themes that emerged in the interview data. A total of 118 participants from a variety of healthcare professionals and leadership positions across five healthcare organizations in the greater Toronto area in Ontario and 1 setting from British Columbia were interviewed. The following three themes were identified during the inductive analysis: 1) prioritizing care based on system capacity, patient volume and complexity; 2) adapting care by innovating, clustering, and taking shortcuts; and 3) being impacted by prioritized and adapted care. Adapting and prioritizing care resulted in missed or delayed care and moral distress in healthcare professionals. Study findings call for leaders to develop and deploy anticipatory adaptive strategies at the organizational level to mitigate pressures related to system capacity and patient volume and complexity. In turn, anticipatory adaptive strategies can guide efforts by healthcare professionals to manage and adapt their clinical tasks, workload, and demands, ensuring patient safety and workforce resilience at the clinical microsystem level.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".