Willingness of University Nursing Students to Volunteer During a Pandemic
Bibliographic record
Abstract
OBJECTIVE: The global threat of an influenza pandemic continues to grow and thus universities have begun emergency preparedness planning. This study examined stakeholder's knowledge, risk-perception, and willingness to volunteer. DESIGN AND SAMPLE: The design of this study is a cross-sectional survey. Questionnaires were sent to 1,512 nursing students and were returned by 484, yielding a response rate of 32% for this subgroup. Nursing students may be a much-needed human resource in the event of an influenza pandemic. MEASURES: The measurement tool was a Web-based questionnaire regarding pandemic influenza designed by a subgroup of researchers on the Public Health Response Committee. RESULTS: Most nursing students (67.9%) said they were likely to volunteer in the event of a pandemic if they were able to do so. An even higher number (77.4%) said they would volunteer if provided protective garments. Overall, 70.7% of students supported the proposition that nursing students have a professional obligation to volunteer during a pandemic. Nursing students indicated that they have had a wealth of volunteer experience in the past and they would apply this service ethic to a pandemic situation. CONCLUSIONS: Emergency preparedness competencies should be integrated into existing nursing curricula and other health science programs. University administrations need to engage in planning to create protocol for recruitment, practice, and protection of volunteers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".