Team Mindfulness in Online Academic Meetings to Reduce Burnout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Burnout, a negative job-related psychological state common with health professionals, results in valuable healthcare research loss. Team mindfulness, promoting work engagement, represents an aspect effective in reducing burnout. In a series of diverse-membership academic meetings intended to reduce research burnout—employing writing prompts, doodling, and continuous developmental feedback—team mindfulness was demonstrated when conducted in person. Therefore, whether team mindfulness is evident when meetings are held online is relevant. During the first eighteen months of COVID-19 limitations requiring these meetings to be online, it was previously reported that team mindfulness was diminished. Question-asking, submitted doodles, and feedback responses were analyzed for the following year of the same group, both quantitively and qualitatively, and with respect to COR theory, to determine if the result persisted. Team mindfulness was also compromised in the second year with respect to the entire group but not regarding the individual relationship with the facilitator. For a diverse-membership group to demonstrate team mindfulness, it is suggested that creating and using avatars similar to those used in online games might be effective. To continue the successful aspect of team mindfulness found online for this group or similarly designed groups, a one-on-one meeting between participant and facilitator is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it