Reasons and decision-making processes for applying to nursing school among nurses showing delayed professional development
Bibliographic record
Abstract
Objectives: The characteristics of nurses who leave the profession after completing nursing school have not been examined sufficiently. Therefore, we examined the reasons for applying to nursing school and the process of arriving at the decision to apply among nurses showing delayed professional development.Methods: Participants were eight junior nurses showing a delay in their growth as nurses, who were working at two teaching hospitals. We utilized semi-structured interviews, and the transcripts were quantitatively and qualitatively analyzed.Results: A frequency analysis of the reasons identified in the transcripts revealed twelve primary reasons for applying to nursing school, such as attraction to the nursing profession, selection from among the different options in the medical field, scholastic aptitude for post-high-school entrance examinations, and academic interest. A qualitative analysis of the process by which participants decided to apply to nursing school yielded three themes: tendency to depend on others, superficial consideration of their own aptitude for the nursing profession, and obtaining a nursing license as a means of accomplishing another purpose.Conclusions: We revealed a number of reasons why newly qualified nurse exhibit delayed professional development as well as three characteristics of their decision-making to apply to a nursing school. The practical implications for the interview process in selection of applicants, effective usage of role model, and coaching are indicated for future nursing education.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".