Willingness to volunteer during an influenza pandemic: perspectives from students and staff at a large Canadian university
Bibliographic record
Abstract
BACKGROUND: A future influenza pandemic will require greater demand on numerous essential services and a reduced capacity to meet that demand. Recruitment of volunteers is an important issue for pre-pandemic planning. OBJECTIVES: To identify factors and attitudes towards volunteerism in the event of a pandemic of influenza. PARTICIPANTS/METHODS: A 42-item web-questionnaire was administered to all faculty, staff and students at the University of Alberta. Respondents indicated their willingness to volunteer. Responses were dichotomized and logistic regression models were developed to capture the association between willingness to volunteer and (i) demographic and information source variables, (ii) risk perception and general knowledge, and (iii) volunteering attitudes and priority access variables. RESULTS: Many factors predicted willingness to volunteer and several involved interactions with other variables. Individuals who were older, relied on University Health Centre information and who had past volunteerism experience were generally more likely to be willing to volunteer. Those willing to volunteer were more likely to think spread could be prevented by covering mouth when coughing/sneezing, and treatment would include drinking fluids. Those who thought influenza would be treated by antibiotics were less willing to volunteer. Likely volunteers thought that healthcare students should be encouraged to volunteer if there was a healthcare worker shortage. CONCLUSION: This study provides guidance for those who are preparing universities to deal with pandemic influenza. The results suggest factors that might be important in the recruitment of volunteers during an influenza pandemic and these factors might be relevant for other sectors as well.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".