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Record W2311710195 · doi:10.3928/0022-0124-20030901-10

Educational Needs of Psychiatric Nurses for Continuing Competency

2003· article· en· W2311710195 on OpenAlexaffabout
Kimberley D Ryan-Nicholls

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2003
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsBrandon University
Fundersnot available
KeywordsCompetence (human resources)Continuing educationNursingMental healthPerceptionPsychologyMedicineDiversification (marketing strategy)Health careMental healthcareMental health nursingPsychiatryMedical educationPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Practice setting diversification has created an increased need for psychiatric nurses to assume more independent roles, while at the same time being able to demonstrate the corresponding degree of competency to practice. Psychiatric nurses were invited to share their perceptions concerning changes occurring in mental health care, proactive strategies for participating in these changes, and educational opportunities to ensure continuing competency to practice. METHOD: Focus groups were conducted with psychiatric nurses located in various Regional Health Authorities in southern and central regions of Manitoba, Canada. RESULTS: Research findings suggest that psychiatric nurses are primarily concerned with balancing the requirement to demonstrate continuing competence to practice with the challenges associated with the evolving mental healthcare system. CONCLUSION: If the requirement for continuing competence is to be reasonable and achievable, it will be essential that the program generate insight into the necessity for such a program to be implemented and acquire support among the registered psychiatric nurse membership.

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)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.008
GPT teacher head0.321
Teacher spread0.313 · 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 designObservational
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

Citations7
Published2003
Admission routes2
Has abstractyes

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