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Record W4283656876 · doi:10.25071/2291-5796.116

One country, two education models: exploring the pedagogical approaches to training undergraduate nurses for mental health care in Canada

2022· article· en· W4283656876 on OpenAlexaffvenueabout
John Jackson, Luke Molloy

Bibliographic record

VenueWitness The Canadian Journal of Critical Nursing Discourse · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMental healthSchismContext (archaeology)SpecialtyNursingCurriculumNurse educationWarrantTraining (meteorology)Mental health careMental health nursingMedicineHealth carePsychologyPsychiatryMedical educationPolitical sciencePedagogyPolitics

Abstract

fetched live from OpenAlex

The training and registration of psychiatric/mental health nurses has a contested past in Canada. One of the consequences of the professional jostling between psychiatry and nursing for control over this area is the unusual circumstances of Canada having two education systems for this specialty. To understand why the schism has taken place and the impact it has had on psychiatric/mental health nursing, the authors have undertaken a critical review of the ontological and epistemological assumptions of these two pedagogical approaches. This review reveals that while the approaches share much in common, groups from both the east and the west receive different levels of mental health-related curriculum within their training. While it could be argued that psychiatric/mental health nursing practice is different enough to warrant its own framework for the preparation of specialist practitioners, there is no clear answer as to whether one of the current models should be implemented over the other. In this context, this paper argues that it is important that psychiatric nurses advocate for a future for the speciality in Canada.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.336
GPT teacher head0.480
Teacher spread0.145 · 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; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes3
Has abstractyes

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