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Record W2081978810 · doi:10.5430/jnep.v3n12p47

Partnership approach: A good practice in teaching mental health

2013· article· en· W2081978810 on OpenAlexvenueno aff
Joy Penman, Debra Populis, Kathryn Cronin

Bibliographic record

VenueJournal of Nursing Education and Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipMental healthBachelorNursingPsychologyMedical educationMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Background/Objective: This paper examines the ternary relations that a Nursing Unit in a regional university campus in South Australia has created in delivering a mental health course to its first-year students studying in the Bachelor of Nursing program. The partnership may be described as three-cornered, where one corner would be the nursing students and the university represented by the academic teaching in the course, another would be the mental health clinicians, and the third corner would be the mental health care workplaces represented by the mental health nurses working with the students during placement. The outcomes of this three-way relationship on students’ course experience and satisfaction is the focus of this paper. Methods: The impact of the partnership in teaching a mental health course on eleven (n=11) students was determined through a twelve-item questionnaire administered at the conclusion of the course. The questionnaire examined students’ experience with the partnership approach, clinician-driven activities, best aspects of the course, impact on learning, and areas for improving future offerings. The collaborative initiative was also evaluated by an academic, three mental health clinicians and three mental health nurses using a modified one-minute questionnaire examining the most important outcome gained from the partnership, the best aspects of the partnership in delivering the course, areas to be included or expanded in the future, and personal and professional impact on university staff, clinicians and nurses. Main findings: The majority of students found the conduct of the mental health course to be a pleasant learning experience. The inclusion of mental health clinicians provided many learning opportunities and afforded a better understanding of the role(s) of mental health nurses. Students felt positive about mental health nursing and some decided that they might pursue the speciality. The best things about the course from the students’ perspective were being close to the reality of mental health practice, learning from real-life experiences, and the opportunity to put theory into practice in the mental health care workplaces. The best aspect of the partnership for the academic and clinicians was helping students better understand mental health issues by creating a real-life learning environment. For the mental health nurses in health care facilities, the best outcome was the opportunity to impart knowledge, broaden career paths, and demystify mental health. Conclusions: This partnership between the university and industry in teaching mental health is a radical departure from traditional university formats. Findings from this pilot study showed an overall satisfaction with the partnership approach in teaching and learning mental health. The partnership was mutually beneficial; it was instrumental in bringing mental health to life, broadening career options for students and building human capacity. This is the start of an on-going academic and industry partnership which will provide the basis for future collaboration opportunities in education, research and clinical practice in a rural and regional setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.134
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.134
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.116
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0220.063
Scholarly communication0.0240.025
Open science0.0060.038
Research integrity0.0150.033
Insufficient payload (model declined to judge)0.0070.004

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 head0.127
GPT teacher head0.565
Teacher spread0.438 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations5
Published2013
Admission routes1
Has abstractyes

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