Mindfulness and meditation - Training our spidey-senses for critical qualitative health research
Bibliographic record
Abstract
I’ve been training students in mindfulness and meditation for over 10 years. What began as a pedagogical survival tool (for my own mental health) soon emerged as something that was incredibly beneficial to my public health students. Turns out sitting in silence, anchoring ourselves in the present moment, and not doing but instead being, is not only good for your physical, social, and emotional health, it’s a great tool for learning. Students report that closing their eyes and sitting together in meditation fosters trust and compassion and builds a sense of community. These feelings allow them to take risks in their learning, to deeply engage with the material, and to participate openly and creatively in ways that foster real growth. During the pandemic I’ve had time to reflect on what I have observed is another benefit of mindfulness and meditation training – that these practices help students develop important skills – or spidey-senses – that are the superpowers of critical qualitative health researchers. These include the ability to be fully present in our work, to listen deeply, to be curious and non-judgemental, to not be attached to outcomes and what we expect to hear or learn, to come to each study and each participant with ‘beginners mind’, to prioritize different ways of knowing, and to accept when things don’t go according to plan (as they always seem to do in qualitative research). In this presentation I welcome all superhero’s and in particular those interested in developing their own and their students spidey-senses. In our time together I’ll share some of the science on why this practice makes good scientists, demonstrate a practice, and provide some tips for those interested in trying it in their own classrooms. During the pandemic I’ve had time to reflect on what I have observed is another benefit of mindfulness and meditation training – that these practices help students develop really important skills – or spidey-senses – that are the superpowers of critical qualitative health researchers. These include the ability to be fully present in our work, to listen deeply, to be curious and non-judgemental, to not be attached to outcomes and what we expect to hear or learn, to come to each study and each participant with ‘beginners mind’, to prioritize different ways of knowing, and to accept when things don’t go according to plan (as they always seem to do in qualitative research). In this presentation I welcome all superhero’s and in particular those interested in developing their own and their students spidey senses. In our time together I’ll share some of the science on why this practice makes good scientists, demonstrate a practice, and provide some tips for those interested in trying it in their own classrooms.
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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 | 0.050 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".