Alberta family physicians’ willingness to work during an influenza pandemic: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Effective pandemic responses rely on frontline healthcare workers continuing to work despite increased risk to themselves. Our objective was to investigate Alberta family physicians willingness to work during an influenza pandemic. DESIGN: Cross-sectional survey. SETTING: Alberta prior to the fall wave of the H1N1 epidemic. PARTICIPANTS: 192 participants from a random sample of 1000 Alberta family physicians stratified by region. MAIN OUTCOME MEASURES: Willingness to work through difficult scenarios created by an influenza epidemic. RESULTS: The corrected response rate was 22%. The most physicians who responded were willing to continue working through some scenarios caused by a pandemic, but in other circumstances less than 50% would continue. Men were more willing to continue working than women. In some situations South African and British trained physicians were more willing to continue working than other groups. CONCLUSIONS: Although many physicians intend to maintain their practices in the event of a pandemic, in some circumstances fewer are willing to work. Pandemic preparation requires ensuring a workforce is available. Healthcare systems must provide frontline healthcare workers with the support and resources they need to enable them to continue providing care.
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.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".