Adapting despite <em>“walls coming down”</em>: Healthcare providers’ experiences of COVID-19 as an implosive adaptation
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has been a daunting exercise in adaptation for healthcare providers. While we are beginning to learn about the challenges faced by teams during the COVID-19 pandemic, what remains underexplored are the strategies team members used to adapt to these challenges. The goal of this study is therefore to explore how healthcare providers navigated and adapted to on-the-ground challenges imposed by COVID-19. METHODS: We interviewed 20 healthcare workers at various hospitals in Ontario, who provided care as part of clinical teams during the COVID-19 pandemic. Data were collected and analyzed following Constructivist Grounded Theory principles including iteration, constant comparison and theoretical sampling. RESULTS: Participants' accounts of their experiences revealed the process of 'implosive adaptation'. The 'reality check', the 'scramble' and the 'pivot' comprised this process. The reality check described the triggers, the scramble detailed the challenges they went through and the pivot prescribed the shifting of mindset as they responded to challenges. These stages were iterative, rather than linear, with blurred boundaries. DISCUSSION: According to our participants, not all adaptations have to be successful during a crisis. The language of reality check, scramble and pivot provides a framework for teams to talk about and make sense of their approaches to crisis, even beyond the COVID-19 pandemic.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".