Analyzing and strengthening the vaccine safety program in Manitoba
Bibliographic record
Abstract
BACKGROUND: The emergence of a novel influenza A virus in 2009 and the rapid introduction of new pandemic vaccines prompted an analysis of the current state of the adverse events following immunization (AEFI) surveillance response in several provinces. OBJECTIVES: To highlight aspects of the situational analysis of the Manitoba Health, Healthy Living and Seniors (MHHLS's) AEFI surveillance system and to demonstrate how common business techniques could be usefully applied to a provincial vaccine safety monitoring program. METHOD: Situational analysis of the AEFI surveillance system in Manitoba was developed through a strengths-weaknesses-opportunities-threats (SWOT) analysis and informed by the National Immunization Strategy vaccine safety priorities. Strategy formulation was developed by applying the threats-opportunities-weaknesses-strengths (TOWS) matrix. RESULTS: Thirteen strategies were formulated that use strengths to either take advantage of opportunities or avoid threats, that exploit opportunities to overcome weaknesses, or that rectify weaknesses to circumvent threats. These strategies entailed the development of various tools and resources, most of which are either actively underway or completed. CONCLUSION: The SWOT analysis and the TOWS matrix enabled MHHLS to enhance the capacity of its vaccine safety program.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".