"The Pure Guidelines of the Monastery Are to be Inscribed in Your Bones and Mind" Dogen (2010, p. 42): Mental Health Nurses'™ Practices as Ritualized Behaviour
Bibliographic record
Abstract
Forms of practice among nurses on acute care mental health units present a way of revealing how different traditions and values are in play between nurses and also within nurses. This paper represents one interpretive theme from a larger, hermeneutic study of nurses’ experiences of nurse-patient relationships on acute care mental health units, using Buddhist perspectives as a resource for interpretation of interviews with nurses. Understandings of ritual in the Zen Buddhist tradition and Catherine Bell’s (2009a) concept of ritualized behavior enabled an interpretive analysis of nurses’ activities as the expression and reflexive reinforcement of underlying traditions, values, and beliefs. In particular, nurses’ preferences among ways of relating with patients evinced contrasting background traditions of confinement and therapeutically directed engagement.AcknowledgementsNo hermeneutic work belongs wholly to its author, and I wish to acknowledge Dr. Shelley Raffin-Bouchal, my doctoral supervisor, and Dr. Nancy Moules, who was a very active member of my supervisory committee for all their guidance and support in conducting the study from which this paper emerged.A version of this paper was presented by the author at the Canadian Hermeneutic Institute, Halifax, Nova Scotia, May 23, 2012. Â
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".