Communities of practice: acknowledging vulnerability to improve resilience in healthcare teams
Bibliographic record
Abstract
The majority of healthcare professionals regularly witness fragility, suffering, pain and death in their professional lives. Such experiences may increase the risk of burnout and compassion fatigue, especially if they are without self-awareness and a healthy work environment. Acquiring a deeper understanding of vulnerability inherent to their professional work will be of crucial importance to face these risks. From a relational ethics perspective, the role of the team is critical in the development of professional values which can help to cope with the inherent vulnerability of healthcare professionals. The focus of this paper is the role of Communities of Practice as a source of resilience, since they can create a reflective space for recognising and sharing their experiences of vulnerability that arises as part of their work. This shared knowledge can be a source of strength while simultaneously increasing the confidence and resilience of the healthcare team.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".