Knowledge, attitude and intentions towards nursing profession among Chinese high school graduates in central China
Bibliographic record
Abstract
Background and objective: Current nursing shortages and low willingness of young people to choose nursing as a career are of major concern in many countries. This study examined the knowledge, attitude and intention towards the nursing profession among high school graduates in China and factors associated with graduate’ intention to enroll in nursing program.Methods: A cross-sectional study was conducted using a random sampling. A total of 3764 high school graduates of selected schools in Central China participated in the study. Data were collected with online survey including demographics, knowledge of and attitude towards nursing and intention to choose nursing. Pearson’s correlations and Hierarchical regression analyses were performed.Results: Less than 10% of the participants expressed interest in a future career in nursing. Knowledge and attitude about nursing were positively associated with intention to study nursing. Both patents’ education, family income and attitude significantly predicted intention to study nursing in the hierarchy regression model (p < .001).Conclusions: Recruitment and retention strategies need to focus on addressing improving knowledge about the roles of nurse among high school students. Nursing administrators and educators should provide information about nursing profession on Websites and social media or programs for high school students with early clinical exposure to nursing to increase their knowledge and change their attitudes toward nursing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".