Preparing for an influenza pandemic: model of an immunization clinic in an urban family practice.
Bibliographic record
Abstract
PROBLEM ADDRESSED: The surge in patient demand for the H1N1 influenza vaccine during the 2009 pandemic. OBJECTIVE OF THE PROGRAM: To facilitate timely delivery of the 2009 H1N1 influenza vaccine to a family practice population while preserving regular clinic function and to create a model of effective vaccination delivery for future outbreaks. PROGRAM DESCRIPTION: An academic family practice in Toronto, Ont, adopted a process-improvement approach and implemented 3 Saturday stand-alone H1N1 vaccination clinics to accommodate increased demand for the vaccine. Medical directives were developed to give nurses the authority to vaccinate patients. Consent forms with eligibility criteria and risks versus benefits sheets were provided to patients in the waiting area to make optimal use of time. The clinic with "appointment blocks" for patients had improved efficiency (ie, fewer bottlenecks from waiting area to vaccination room), which was satisfactory to both staff and patients. CONCLUSION: During a pandemic, when patient demand for vaccination is high, such stand-alone vaccination clinics in conjunction with family practices can deliver vaccines to patients in a timely and acceptable manner while promoting continuity of care. This model requires the commitment of extra staffing resources if regular primary care delivery is to be maintained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".