The Value of Communities of Practice as a Learning Process to Increase Resilience in Healthcare Teams
Bibliographic record
Abstract
Abstract This paper addresses the role that communities of practice (CoP) can have within the healthcare environment when facing uncertainty and highly emotionally impactful situations, such as the current COVID-19 pandemic. The starting point is the recognition that CoPs can contribute to build resilience among their members, and particularly moral resilience. Among others, this is due to the fact that they share a reflective space from which shared knowledge is generated, which can be a source of strength and trust within the healthcare team. Specifically, in extreme situations, the CoPs can contribute to coping with moral distress, which will be crucially important not only to facing crisis situations, but to prevent the long-term adverse consequences of working in conditions of great uncertainty. The purpose of this paper is to analyze how CoP can support healthcare professionals when building moral resilience. To support that goal, we will first define CoP and describe the main characteristics of communities of practice in healthcare. Subsequently, we will clarify the concept of moral resilience, and establish the relationship between CoP and moral resilience in light of the current COVID-19 pandemic. Finally, we analyze different group experiences that we can consider as CoP which emerged in the midst of the COVID-19 pandemic to navigate moral problems that arose.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".