Enhancing public health response to respiratory epidemics: are family physicians ready and willing to help?
Bibliographic record
Abstract
OBJECTIVE: To describe Ottawa family physicians' perceptions of their preparedness to respond to outbreaks of infectious diseases or other public health emergencies and to assess their capacity and willingness to assist in the event of such emergencies. DESIGN: Cross-sectional self-administered survey conducted between February 11 and March 10, 2004. SETTING: The City of Ottawa, Ont, and the Department of Family Medicine at the University of Ottawa. PARTICIPANTS: Ottawa family physicians; respondents can be considered a self-selected sample. MAIN OUTCOME MEASURES: Self-reported office preparedness and physicians' capacity and willingness to respond to public health emergencies. RESULTS: Response rate was 41%. Of 676 physicians contacted, 274 responded, and of those, 246 completed surveys. About 26% of respondents felt prepared for an outbreak of influenza not well covered by vaccine. About 18% felt prepared for serious respiratory epidemics, such as severe acute respiratory syndrome; about 50% felt unprepared. Most respondents (80%) thought they were not ready to respond to an earthquake. About 77% of physicians were willing to be contacted on an urgent basis in case of a public health emergency. Of these, 94% would assist in immunization clinics, 84% in antibiotic clinics, 58% in assessment centres, 52% in treatment centres, 41% with declaration of death, 26% with home care, and 23% with telephone counseling. CONCLUSION: Family physicians appear to be unprepared for, but willing to address, serious public health emergencies. It is essential to set up effective partnerships between primary care and public health services to support family physicians' capacity to respond to emergencies. This type of study, along with the creation of a register of available services and of a virtual network for sharing information, is an initial step in assessing primary care response.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".