The Facilitators and Barriers to Nurses’ Participation in Continuing Education Programs: A Mixed Method Explanatory Sequential Study
Bibliographic record
Abstract
BACKGROUND: Since several factors affect nurses' participation in Continuing Education, and that nurses' Continuing Education affects patients' and community health status, it is essential to know facilitators and barriers of participation in Continuing Education programs and plan accordingly. This mixed approach study aimed to investigate the facilitators and barriers of nurses' participation, to explore nurses' perception of the most common facilitators and barriers. METHODS: An explanatory sequential mixed methods design with follow up explanations variant were used, and it involved collecting quantitative data (361 nurses) first and then explaining the quantitative results with in-depth interviews during a qualitative study. RESULTS: The results showed that the mean score of facilitators to nurses' participation in Continuing Education was significantly higher than the mean score of barriers (61.99 ± 10.85 versus 51.17 ± 12.83; p<0.001, t=12.23). The highest mean score of facilitators of nurses' participation in Continuing Education was related to "Update my knowledge". By reviewing the handwritings in qualitative phase, two main levels of updating information and professional skills were extracted as the most common facilitators and lack of support as the most common barrier to nurses' participation in continuing education program. CONCLUSION: According to important role Continuing Education on professional skills, nurse managers should facilitate the nurse' participation in the Continues Education.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".