Are Skills Learned in Nursing Transferable to Other Careers?
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of skills gained in nursing on the transition to a non-nursing career. BACKGROUND: Little is known about the impact that nursing skills have on the transition to new careers or about the transferability of nursing skills to professions outside nursing. METHOD: A postal questionnaire was mailed to respondents who had left nursing. The questionnaire included demographic, nursing education and practice information, reasons for entering and leaving nursing, perceptions of the skills gained in nursing and the ease of adjustment to a new career. Data analysis included exploratory and confirmatory factor analysis, Pearson product moment correlations and linear and multiple regression analysis. RESULTS: Skills learned as a nurse that were valuable in acquiring a career outside nursing formed two factors, including "management of self and others" and "knowledge and skills learned," explaining 32% of the variation. The highest educational achievement while working as a nurse, choosing nursing as a "default choice," leaving nursing because of "worklife/homelife balance" and the skills of "management of self and others" and "knowledge and skills" had a significant relationship with difficulty adjusting to a non-nursing work role and, overall, explained 28% of the variation in this difficulty adjusting. CONCLUSION: General knowledge and skills learned in nursing prove beneficial in adjusting to roles outside nursing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".