Becoming a nurse: a meta‐study of early professional socialization and career choice in nursing
Bibliographic record
Abstract
AIM: This paper is a report of a meta-study of early professional socialization and career choice in nursing. BACKGROUND: The current and growing shortage of nurses is a global issue, and nursing recruitment and retention are recognized priorities internationally. The future of nursing will lie in the ability to recruit and retain the next generation to the profession. DATA SOURCES: Studies were identified through a search of the CINAHL, PsycInfo, Sociological Abstracts, PubMed; Medline and Embase databases from 1990 to 2007. REVIEW METHODS: Studies were included if they gave insight into the experience of choosing nursing as a career, used qualitative methodology and methods, and were published in English. Analysis was undertaken using Paterson et al.'s framework for qualitative meta-synthesis. RESULTS: Ten primary studies were included in the review. Their methodologies included: ethnography (4); descriptive qualitative (3); grounded theory (2); and phenomenology (1). The location of the research was Canada (3), United Kingdom (2), United States of America (2), Australia (1), Japan (1) and Sweden (1). Three main themes were identified: influence of ideals; paradox of caring and role of others. CONCLUSION: Career choice and early professional socialization are influenced by multiple factors. In future recruitment and retention strategies to address the critical nursing shortage, it is important to consider the role of mentors, peers and role models in the formulation of career expectations, and career choice decisions. It is also necessary to consider the role of mentors, peers and role models in the formulation of career expectations, and career choice decisions.
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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 | 0.039 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".