Iranian Clinical Nurses’ Readiness for Self-Directed Learning
Bibliographic record
Abstract
INTRODUCTION: 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. AIMS: 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. METHODS: 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. FINDINGS: 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. CONCLUSION: 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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".