Awakening the Sensible Being (ASB) as experienced by physicians: development of a theoretical model
Bibliographic record
Abstract
Background / Purpose: Awakening the “Sensible” Being (ASB) is a formative practice geared toward care giving and support. It examines how experiencing one’s own body and its movement stimulates the development of self-awareness and awareness of others, both of which are desirable qualities for healthcare professionals. To our knowledge there is no theoretical model regarding the process that occurs and its potential effects on participants.Based on our research with physicians having undergone ASB training, we developed such a preliminary model. Methods: Grounded theory was used for a secondary analysis of thesis data (Lachance 2016). This analysis was inspired by the relationship between learning and caring (Honoré 1992, 2003) and the integration of consciousness according to Newman’s (1990, 1997) concept of health. Results: The model is one of concentric circles. ASB training fosters the development of an internal dialogue in individuals (center of the model) which has effects on their personal life and by ricochet, their professional activities (periphery of the model). Their expanded sense of self-awareness and quality of “savoir-être” brought on by the training contributes to their impression of “feeling whole” and provides them with a quality of presence that influences the type of care they can provide to their patients by considering the individual as a whole. Conclusion: Behind a physician, there is a human being with human qualities enabling them to be a better physician. Our model underlines the importance of developing the inner self as a background to becoming a better health professional.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".