Incorporating Scannable Forms into Immunization Data Collection Processes: A Mixed-Methods Study
Bibliographic record
Abstract
INTRODUCTION: Individual-level immunization data captured electronically can facilitate evidence-based decision-making and planning. Populating individual-level records through manual data entry is time-consuming. An alternative is to use scannable forms, completed at the point of vaccination and subsequently scanned and exported to a database or registry. To explore the suitability of this approach for collecting immunization data, we conducted a feasibility study in two settings in Ontario, Canada. METHODS AND FINDINGS: Prior to the 2011-2012 influenza vaccination campaign, we developed a scannable form template and a corresponding database that captured required demographic and clinical data elements. We examined efficiency, data quality, and usability through time observations, record audits, staff interviews, and client surveys. The mean time required to scan and verify forms (62.3 s) was significantly shorter than manual data entry (69.5 s) in one organization, whereas there was no difference (36.6 s vs. 35.4 s) in a second organization. Record audits revealed no differences in data quality between records populated by scanning versus manual data entry. Data processing personnel and immunized clients found the processes involved to be straightforward, while nurses and managers had mixed perceptions regarding the ease and merit of using scannable forms. Printing quality and other factors rendered some forms unscannable, necessitating manual entry. CONCLUSIONS: Scannable forms can facilitate efficient data entry, but certain features of the forms, as well as the workflow and infrastructure into which they are incorporated, should be evaluated and adapted if scannable forms are to be a meaningful alternative to manual data entry.
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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 | 0.089 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".