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Pedagogy, power and practice ethics: clinical teaching in psychiatric/mental health settings

2007· article· en· W2014948875 on OpenAlexaff
Carol Ewashen, Annette Lane

Bibliographic record

VenueNursing Inquiry · 2007
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPracticumMental healthActive listeningPsychologyPower (physics)NegotiationProfessional ethicsPedagogyMedical educationNursingMedicinePsychotherapistEngineering ethicsSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.136
metaresearch head score (Gemma)0.095
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1360.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.029
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.235
GPT teacher head0.652
Teacher spread0.417 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreCommentary

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".

Quick stats

Citations23
Published2007
Admission routes1
Has abstractyes

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