MétaCan
Menu
Back to cohort
Record W2052543647 · doi:10.3928/00220124-20091119-05

Perceptions of Select Registered Nurses of the Continuing Competence Program of the Saskatchewan Registered Nurses' Association

2009· article· en· W2052543647 on OpenAlexaffabout
Sandra Bassendowski, Pammla Petrucka

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
Fundersnot available
KeywordsCompetence (human resources)Continuing educationAssociation (psychology)NursingPerceptionMedicinePsychologyFamily medicineMedical educationRegistered nurseSocial psychology

Abstract

fetched live from OpenAlex

Nursing is a self-regulating profession, and most professional nursing jurisdictions across Canada have undertaken the creation of Continuing Competence Programs (CCPs), with the goals of promoting good nursing practice, encouraging continuous learning, contributing to the quality of nursing practice, and optimizing client outcomes. Most CCPs call for a professional portfolio to collect, synthesize, and analyze professional experiences, including documentation of peer feedback and preparation of a learning plan. In the province of Saskatchewan, Canada, there is a self-reflective tool that enables registered nurses to self-rate their achievement of a set of foundational competencies. This article explores the perceptions that select registered nurses have about the CCP in Saskatchewan and how their view of the degree of professional control (as assessed through locus of control) that they have affects their perspective about the outcomes of the program. The study was designed to assess how perceived locus of control was related to how registered nurses view the implementation of the CCP in Saskatchewan.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.432
Teacher spread0.406 · 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".

Quick stats

Citations7
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Continuing Education in NursingSame topicGlobal Health Workforce IssuesFrench-language works237,207