A World Café Discussion on Well-Being: Considerations for Life in the University
Bibliographic record
Abstract
How are universities conceptualizing and mobilizing well-being on their campuses? Our qualitative inquiry explores growing challenges of addressing educator mental health and well-being on university campuses. As part of an effort to increase awareness and support around issues of mental health and well-being at one university, a campus-wide strategy was announced in 2015. This article follows up on that strategy to understand how university educators come to identify with well-being. We collected composite anonymized data from a World Café discussion with a range of educators. The goals of the World Café discussion were to: (a) highlight campus-wide conversations on educator mental health and well-being; (b) explore multiple perspectives and make sense of how educators experience mental health and well-being; (c) create a space to nurture meaningful relationships; (d) inform the continued development of research, strategies, and policies to support educator mental health and well-being. We share four themes that emerged from the discussions to consider well-being and life in the university: (a) affective, relational and holistic aspects “in search of well-being”; (b) working through the messiness of well-being: risks and vulnerabilities; (c) inviting people into a culture of well-being; and (d) the role of leaders in moving beyond policy towards enactment.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".