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Record W2322670653 · doi:10.3928/00220124-20070301-03

Barriers to Participation in Continuing Education Activities Among Rural and Remote Nurses

2007· article· en· W2322670653 on OpenAlexaffabout
Kelly Penz, Carl D’Arcy, Norma J. Stewart, Julie Kosteniuk, Debra Morgan, Barbara S. Smith

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsContinuing educationNursingPerceptionJob satisfactionRural areaIsolation (microbiology)Continuing professional developmentNurse educationPsychologyMedicineMedical educationProfessional development

Abstract

fetched live from OpenAlex

BACKGROUND: This article examines the barriers to participation in continuing education activities that are perceived by rural and remote registered nurses in Canada. METHODS: The data are drawn from a national survey that was part of a larger national project, "The Nature of Nursing Practice in Rural and Remote Canada." RESULTS: Perceived barriers to participation in continuing education activities include the isolation of rural nurses and time and financial constraints. Nurses who perceived barriers to participation were more likely to be middle-aged, unmarried, and working full-time than nurses who did not perceive barriers. They were also more likely to possess higher levels of nursing education and have children or dependents. The perception of barriers to participation was also associated with lower job and scheduling satisfaction. CONCLUSIONS: Rural and remote registered nurses have moderately high levels of participation in continuing education; however, participation and job satisfaction can be improved if some of the barriers identified are addressed.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.444
Teacher spread0.431 · 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

Citations137
Published2007
Admission routes2
Has abstractyes

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