Pedagogy, power and practice ethics: clinical teaching in psychiatric/mental health settings
Bibliographic record
Abstract
Often, baccalaureate nursing students initially approach a psychiatric mental health practicum with uncertainty, and even fear. They may feel unprepared for the myriad complex practice situations encountered. In addition, memories of personal painful life events may be vicariously evoked through learning about and listening to the experiences of those diagnosed with mental disorders. When faced with such challenging situations, nursing students often seek counsel from the clinical and/or classroom faculty. Pedagogic boundaries may begin to blur in the face of student distress. For the nurse educator, several questions arise: Should a nurse educator provide counseling to students? How does one best negotiate the boundaries between 'counselor', and 'caring educator'? What are the limits of a caring and professional pedagogic relation? What different knowledges provide guidance and to what differential consequences for ethical pedagogic relationships? This paper offers a comparative analysis of three philosophical stances to examine differences in key assumptions, pedagogic positioning, relationships of power/knowledge, and consequences for professional ethical pedagogic practices. While definitive answers are difficult, the authors pose several questions for consideration in discerning how best to proceed and under what particular conditions.
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.
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".