Supporting researcher resilience in emotionally demanding research work: building and sustaining an international community of practice
Bibliographic record
Abstract
Researching emotionally demanding topics, like gender-based violence, can have potential personal impacts, including poor mental health and vicarious trauma. Researchers need support when working on such topics. The purpose of this article is to describe how an international Community of Practice (CoP) for researchers exploring sensitive topics was established to support resilience building practices. The Researcher Resilience Community of Practice (RRCoP) was initiated by three leaders from international universities with a shared interest in researcher resilience and wellbeing. Virtual meetings, held every other month, centre on relationship building, developing resilience skills, and connecting with researchers in the field of supporting graduate student wellbeing. The RRCoP is open to anyone involved in research and provides a space for emotional debriefing, promoting a sense of belonging, understanding, and reduced isolation among members. Workshops and presentations contribute to members’ personal resources for resilience/wellbeing. Meanwhile, working groups within the RRCoP actively pursue tangible changes in the field of supporting researcher wellbeing. This article presents member reflections on personal impact of CoP engagement and recommendations for future growth. The RRCoP continues to foster researcher resilience and wellbeing in sensitive research. This peer support network serves an important role in mitigating negative impacts of research work.
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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 | 0.107 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.006 | 0.053 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".