Clinical learning in nursing education as a factor to enhance organizational socialization in newcomer nurses
Bibliographic record
Abstract
Background : Clinical learning in nursing education has a pivotal role in enhancing clinical competences of nursing students. Moreover it provides an anticipatory knowledge of the organizational contexts in which nursing care is delivered. The aim of this study was to demonstrate the role of clinical learning in nursing education to enhance post-graduation organizational entry. Method : A retrospective cross-sectional design was used. A sample of 250 newcomer nurses was enrolled in hospital settings. The mean age was 32.1 years (SD 7.95) and 79.2% participants were female. An adaptation of 3 items of Clinical Learning Environment and Supervision Scale, Organizational Socialization Inventory and validated items to assess turnover intention and therapy errors rate were used. Structural Equation Modelling was performed. Results : Clinical learning experienced in undergraduate education positively correlated with newcomers' organizational socialization (β = 0.41, p < .001), organizational socialization contributed to reducing turnover intention (β = -0.67, p < .001) and therapy errors rate (β = -0.24, p = .003). The model’s fit was good (RMSEA = 0.050, CFI = 0.971, TLI = 0.963, SRMR = 0.045). Conclusion : Undergraduate nursing education is an important phase to enhance an effective organizational socialization. Nursing education institutions and health care settings need to conjointly work to provide effective clinical settings for nursing education, in order to enhance both clinical learning and organizational outcomes.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".