Transprofessional competencies across clinical, organisational, and educational professions: the case of mindfulness-based teaching and learning (MBTL)
Bibliographic record
Abstract
This study explores mindfulness-based teaching and learning (MBTL) as an emerging field of transprofessional practice spanning educational, organisational, and clinical professions. Recognising the need for a more robust set of transprofessional MBTL teacher competencies to serve this emerging specialisation, the authors developed and validated the Mindfulness-Based Teaching and Learning – Teacher Competency Framework (MBTL-TCF). Building on the pre-existing Mindfulness-Based Interventions-Teaching Assessment Criteria (MBI-TAC), the researchers developed a teaching framework for mindfulness specialists to reflect teacher agency, autonomy, and self-determination consistent with the purposes, traditions, and effects of what MBTL teaches: that is, mindfulness. The paper presents the sequence of construct, face, and content validation procedures, including the alignment of the MBTL-TLC with Dreyfus and other teacher competency frameworks from a range of sectors and countries. Finally, using an adapted Delphi process, a six-member international expert panel plus one diversity reviewer were invited to review and refine the emerging framework. The resulting MBTL-TCF presents 12 competency domains with associated activities and performance indicators.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".