STUDY PROTOCOL Open Access Study protocol
Bibliographic record
Abstract
Background: The collection of individual-level pandemic (H1N1) 2009 influenza immunization data was considered important to facilitate optimal vaccine delivery and accurate assessment of vaccine coverage. These data are also critical for research aimed at evaluating the new vaccine's safety and effectiveness. Systems used to collect immunization data include manual approaches in which data are collected and retained on paper, electronic systems in which data are captured on computer at the point of vaccination and hybrid systems which are comprised of both computerized and manual data collection components. This study's objective was to compare the efficiencies and perceptions of data collection methods employed during Canada's pandemic (H1N1) 2009 influenza vaccination campaign. Methods/Design: A pan-Canadian observational study was conducted in a convenience sample of public health clinics and healthcare institutions during the H1N1 vaccination campaign in the fall of 2009. The study design consisted of three stages: Stage 1 involved passive observation of the site's layout, processes and client flow; Stage 2 entailed timing site staff on 20 clients through five core immunization tasks: i) client registration, ii) medical history collection, iii) medical history review, iv) vaccine administration record keeping and v) preparation of proof of vaccine administration for the client; in Stage 3, site staff completed a questionnaire regarding perceived usability of the site's
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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 | 0.050 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.319 | 0.066 |
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".