The Nursing Shortage: Is It Really About Image?
Bibliographic record
Abstract
A poor public "image" of the nurse is believed to contribute to nurse shortages. We surveyed more than 3,000 college students in science and math courses in a seven-county region of California's Central Valley to assess their perceptions of a career as a nurse in relation to a career as a physical therapist, a high school teacher, or a physician. Students generally had favorable perceptions of nursing, with two-thirds agreeing that nursing has good income potential, job security, and interesting work. However, nursing lagged behind the other occupations in perceptions of independence at work and was more likely to be perceived as a "women's" occupation. Our findings suggest that these college students have generally gotten the message that nursing is a financially rewarding and desirable career, although they also perceive nursing to be less attractive on some important occupational characteristics such as job independence. Unless nursing training capacity expands substantially, the projected nurse shortage will occur. With continued aggressive marketing of nursing as a career, there is a risk of engendering a backlash from prospective students frustrated in their effort to find a slot in a nursing training program. Much work remains to be done to alter the image of nursing as a women's occupation and to transform the work environment of nurses to make a career in nursing more attractive.
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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 | 0.002 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".