College Students' Perceptions of Nursing: A GEE Approach
Bibliographic record
Abstract
The nursing shortage has stimulated renewed attention to understanding factors that may enhance the recruitment of students into nursing programs and the retention of registered nurses in the workforce. Many activities have been initiated to address the shortage of nurses, including increasing recruitment of students to study nursing. This paper has two major goals: (1) to answer the research question, "To what extent do college students' characteristics explain the differences in their attitudes towards four service occupations (nursing, medicine, physical therapy and high school teaching)?" and (2) to demonstrate statistical methods appropriate for performing multivariate analyses of clustered data and merging independent survey items into a clustered, multivariate analysis for direct comparison of the different items. Results indicate that the more favourable rating of nursing as an occupation relative to physical therapy is due to the sample, including a large number of students majoring in nursing. Students who are not nursing majors do not appear to hold a more favourable attitude towards nurses relative to physical therapists. The lower rating of high school teachers and higher rating of physicians on most items persists even after adjusting for all the control variables, including whether or not students are nursing majors. Additionally, results support the need for a statistical method such as generalized estimating equations (GEE) to account for individual and interaction confounders, repeated measures, clustering and correlated data.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".