Career decisions and aspirations of early‐career nurses: Insights from a qualitative interpretative description study
Bibliographic record
Abstract
AIM: To explore the career decisions and aspirations of early-career registered nurses in New Brunswick, Canada. DESIGN: A qualitative study using an interpretive description approach was conducted. METHODS: Semi-structured one-on-one interviews were conducted with a purposive sample of nurses (n = 22) currently working in New Brunswick, Canada, with up to 5 years of experience from February to April 2022. RESULTS: Participants described diverse career paths and aspirations. Personal factors affecting these included the desire for meaningful work, career satisfaction, work-life balance, spending time with family, working in a preferred location, and finances. Professionally, working conditions were the dominant factor influencing early-career nurses' career decisions and aspirations. Participants described how short staffing, safety, support, and scheduling influenced their day-to-day work, mental and physical health, job and career satisfaction, and intent to leave. CONCLUSION: The findings highlighted the abundant and diverse career opportunities available to nurses early in their careers. Early-career nurses are interested in finding nursing positions with a high degree of person-job fit and value opportunities for ongoing professional education and growth. IMPACT: This study in New Brunswick, Canada, explores early-career nurses' career decisions and aspirations during nursing shortages and the pandemic, emphasizing the importance of person-job fit. Recommendations include improving working conditions and career pathways to enhance the sustainability of the nursing profession. REPORTING METHOD: Standards for Reporting Qualitative Research (SRQR). PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".