The history of nurse imagery and the implications for recruitment: a discussion paper
Bibliographic record
Abstract
AIM: This paper presents a discussion of the history of nurse imagery in the context of recent career choice research and the need for contemporary images for nursing recruitment. BACKGROUND: The critical and growing shortage of nurses is a global concern. Understanding how individuals come to know nursing as a career choice is of critical importance. Stereotypical imaging and messaging of the nursing profession have been shown to shape nurses' expectations and perceptions of nursing as a career, which has implications for both recruitment and retention. DATA SOURCES: Relevant research and literature on nurse imagery in relation to career choice and recruitment were identified through a search of the CINAHL, PsychINFO, Sociological Abstracts, PubMed; Medline and Embase databases from 1970-2012. DISCUSSION: Historical images of nurses and nursing remain prevalent in society today and continue to influence the choice of nursing as a career among the upcoming generation of nurses. Students interested in nursing may be dissuaded from choosing it as a career based on negative, stereotypical images, especially those that position the profession as inferior to medicine. IMPLICATIONS FOR NURSING: Understanding the evolution and perpetuation of popular images and messages in relation to the profession has implications for not only how we recruit and retain future generations of professional nurses but also holds implications for interprofessional collaboration between nursing and other health disciplines. CONCLUSION: Strategies for future recruitment and socialization within the nursing and the health professions need to include contemporary and realistic imaging of both health professional roles and practice settings.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".