Perceptions of immunization information systems for collecting pandemic H1N1 immunization data within Canada's public health community: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Immunization information systems (IISs) are electronic registries used to monitor individual vaccination status and assess vaccine coverage. IISs are currently not widely used across Canada, where health jurisdictions employ a range of approaches to capture influenza immunization information. Conducted in advance of the 2009 H1N1 vaccination campaign, the objectives of this study were to understand the perceived value of individual-level data and IISs for influenza control, identify ideal system functions, and explore barriers to implementation. METHODS: In July and August 2009, semi-structured interviews were conducted with key informants engaged in vaccine delivery and/or pandemic planning at regional, provincial/territorial and federal levels across Canada. Key informants were recruited using a combination of convenience and snowball sampling methodologies. Qualitative analysis was used to extract themes from interview content. RESULTS: Patient management, assessment of vaccine coverage, and evaluation of safety and effectiveness were identified as public health priorities that would be achieved in a more timely manner, and with greater accuracy, through the use of an IIS. Features described as ideal included system flexibility, rapid data entry, and universality. Financial and human resource constraints as well as coordination between immunization providers were expressed as barriers to implementation. CONCLUSIONS: IISs were perceived as valuable by key informants for strengthening management capacity and improving evaluation of both seasonal and pandemic influenza vaccination campaigns. However, certain implementation restrictions may need to be overcome for these benefits to be achieved.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".