Challenges of fresh nursing graduates during their transition period
Bibliographic record
Abstract
Objective: The shortage of nurses is an overwhelming problem worldwide. Numerous studies indicate that fresh nursing graduates encounter many challenges in their first year after graduation. These difficulties affect their psychological health and influence their perseverance which results in a high resignation rate. Hong Kong is not an exceptional case; therefore, the aim of this study was to explore the challenges encountered by fresh nursing graduates during the transition period in order to provide insights to academics and clinical administrators in order to facilitate the transition and alleviate the negative impacts, thus increasing the retention rate.Methods: This was a qualitative study and eight new nursing graduates (M = 4; F = 4) from the same local higher education institute were interviewed individually. Thematic coding was used to analyse the data.Results: Finally, nine themes were identified including eight areas of challenges and one common attribute. Workload, lack of knowledge, communication, expectation, change of role, working atmosphere, support and a blame/complaint culture are the common areas of challenges that they encounter in the transitional period. Furthermore, this study also found that new nursing graduates possess a common attribute, i.e. positive personal attitude which seems able to enhance their perseverance in this period.Conclusions: The identified themes are interrelated and all the stakeholders should join together and form a cycle of continuous improvement in order to improve the nursing programme and clinical supports to the fresh nursing graduates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".