Competence of Nurses Relating Self-Directed Learning in Saudi Arabia: A Meta-Analysis
Bibliographic record
Abstract
Self-directed learning (SDL) has become important for medical students for developing their independent learning skills. This may help them in enhancing their sense of assertiveness and responsibility. Therefore, the present study aims to evaluate the competency based learning among the nurses as it is important to secure and promote the nursing services. The study has conducted a broad spectrum of research studies between the 10-year duration i.e., between the time-period of (2009–2018). The studies shedding light on details of the competence of nurses’ relation to SDL in Saudi Arabia were reviewed. The results have shown significant post-intervention enhancements with a pooled random-effects standardized mean difference of 0.81. The effect size of learning was higher as compared to the reaction with respect to the assessment level. It has also been shown that students with medium-competence simulation have higher effect size as compared to high-competence simulation and low-competence simulation.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".