Effectiveness of continuing education programmes in nursing: literature review
Bibliographic record
Abstract
AIM: The aim of this paper is to review the literature on what facilitates or inhibits continuing education in nursing and to identify ways to make continuing education more effective. BACKGROUND: [corrected] Healthcare professionals have always been encouraged to update their knowledge and maintain clinical competence. The rapid changes currently taking place within healthcare systems have increased the pressure from direct care providers, professional bodies and the general public for nurses to engage in continuing education programmes. Despite a growing body of empirical research on this topic, the effectiveness and impact of continuing education remains underexplored. METHOD: A literature search was conducted in January 2005 using CINAHL, Medline, the Cochrane databases and the Internet. Keywords used were: 'continuing education', 'professional development', 'viability of continuing education/professional development programmes', 'evaluation of continuing education/professional development programmes' and 'effectiveness of continuing education/professional development programmes'. No date restrictions were imposed. RESULTS: Factors that facilitate the implementation of continuing education in nursing arise from individual, professional and organizational perspectives. While the philosophy behind continuing education is to encourage nurses to become lifelong learners, the learning method chosen for such programmes is often didactic in nature, as opposed to encouraging nurses to take initiative and direct their own learning. Continuing education is intended to ensure healthcare practitioners' knowledge is current, but it is difficult to determine if those who attend these courses are implementing what they have learnt. CONCLUSION: To make continuing education programmes more effective, nurses need to have a more participatory role in their learning. A concerted effort should be made to make continuing education attainable and realistic.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".