Iranian Clinical Nurses’ Readiness for Self-Directed Learning
Bibliographic record
Abstract
<p><strong>INTRODUCTION</strong><strong>:</strong> Clinical nurses are in need of being able to adapt to the ever-changing environment of clinical settings. The prerequisite for their successful adaptation is to be lifelong learners. An approach for making nurses lifelong learners is self-directed learning.</p> <p><strong>AIMS</strong><strong>:</strong> This study was undertaken to evaluate a group of Iranian clinical nurses’ readiness for self-directed learning and its relationship with some of their personal characteristics.</p> <p><strong>METHODS</strong><strong>:</strong> This cross-sectional descriptive study was conducted in 2014. A random sample of 314 nurses working in three hospitals affiliated to Isfahan Social Security Organization, Isfahan, Iran, was recruited to complete the Fisher’s Self-directed Learning Readiness Scale.</p> <p><strong>FINDINGS</strong><strong>:</strong> In total, 279 nurses filled the scale completely. The mean of their readiness for self-directed learning was 162.50±14.11 (120–196). The correlation of self-directed learning readiness with age, gender, marital status, and university degree was not statistically significant.</p> <p><strong>CONCLUSION</strong><strong>:</strong> Most nurses had great readiness for self-directed learning. Accordingly, nursing policy-makers need to develop strategies for promoting their self-directed learning. Moreover, innovative teaching methods such as problem solving and problem-based learning should be employed to prepare nurses for effectively managing the complexities of their ever-changing work environment.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".