Learning About Resilience from Rural Interprofessional Healthcare Teams: Insights from the “First Wave” of COVID-19
Bibliographic record
Abstract
Building resilience is a key concern for adult educators today as we face unprecedented global challenges such as the coronavirus disease-19 (COVID-19). Nowhere is this more apparent than in educational initiatives with health professionals who experience many stressors in their work, now amplified by the pandemic. This paper reports the results of focus groups with three interprofessional primary healthcare teams in rural Nova Scotia, Canada, in the fall of 2021. The aim was to learn about their lived experience during the first year of the pandemic, as a basis for considering how resilience could be nurtured and supported in rural team-based collaborative practices settings. Findings reveal that, while each collaborative team experienced recognized COVID-19 workplace stressors, they leveraged a store of collective resilience to navigate the pandemic. The trust, sense of purpose, and shared problem-solving skills they derived from working in collaborative structures over time enabled them to regain equilibrium and to adapt to new norms, and to transform thier practices. The study highlights the power of collaborative learning to strengthen overall ability for resilient performance, and the adaptive capacity that is required to deliver and sustain quality healthcare. The study highlights the need for continuing professional education that values naturally occurring practice-based learning. Adult educators are well positioned to support health professionals and health systems to nurture and support resilient action. They bring an understanding of effective collaborative tools and processes that foster dialogue and collective awareness that leads to a shared identity and understanding. As this study reveals, it is this shared identity and capacity arising within a group that enables them to draw on their collective sources of support to deal with adversity.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 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".