A Cross-Sectional Study of Facilitators and Barriers of Iranian Nurses' Participation in Continuing Education Programs
Bibliographic record
Abstract
BACKGROUND: Continuing education is one of the modern strategies to maintain and elevate knowledge and professional skills of nurses which in turn elevate the health status of society. Since several factors affect nurses' participation in continuing education, it's essential to know promoters and obstacles in this issue and plan accordingly. METHODS: In this cross-sectional study, 361 Iranian nurses who were recruited by convenience sampling method completed an anonymous, self-administered questionnaire from October 2012 to April 2013. Topics covered the participants' attitudes towards facilitators and barriers of their participation in continuing education. RESULTS: Mean and standard deviation of participants ' age were 37.14±7.58 years and 93.94% were female. The maximum score of facilitators and barriers to nurses' participation in continuing education were related to "Update my knowledge" and "Work commitments" respectively. The results showed among Iranian nurses, the mean score of personal and structural barriers was significantly higher than the mean score of interpersonal ones (F=2122.66, p<0.001). CONCLUSION: Results highlight policy makers and nursing managers' role on improving the accessibility to provided continuing education programs by enforcement of facilitators and reducing barriers focusing on the personal and structural barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".