Willingness to volunteer during an influenza pandemic: perspectives from students and staff at a large Canadian university
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it