Difficulties experienced by newly-graduated nurses in Turkey: A qualitative study of the first six months of employment
Bibliographic record
Abstract
Objective : The aim of this study was to identify the difficulties experienced by newly-graduated nurses within the first six months of employment. Methods : This research was designed as a qualitative study based on content analysis. The study sample consisted of nurses who had graduated from nursing in 2010, have been working at a public university hospital in Istanbul for at least six months, and were selected via “maximum variation sampling” from purposeful sampling methods. Data were collected via semi-structured in-depth interviews. The data collection process ended at the end of the fifteenth interview when data saturation was reached. Results : The results obtained based on the qualitative sampling isolated four general themes explaining the difficulties newly-graduated nurses experience within the first six months of their employment: lack of knowledge, heavy workload, lack of clinical skills and communication difficulties. Conclusions : The findings of this first qualitative study conducted with nurses in Turkey are in support of the related literature. Among the difficulties experienced by the newly-graduated nurses within the first months of employment, educators should address the issue of lack of knowledge and managers should address the other issues raised.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".