The integration of barcode scanning technology into Canadian public health immunization settings
Bibliographic record
Abstract
BACKGROUND: As part of a series of feasibility studies following the development of Canadian vaccine barcode standards, we compared barcode scanning with manual methods for entering vaccine data into electronic client immunization records in public health settings. METHODS: Two software vendors incorporated barcode scanning functionality into their systems so that Algoma Public Health (APH) in Ontario and four First Nations (FN) communities in Alberta could participate in our study. We compared the recording of client immunization data (vaccine name, lot number, expiry date) using barcode scanning of vaccine vials vs. pre-existing methods of entering vaccine information into the systems. We employed time and motion methodology to evaluate time required for data recording, record audits to assess data quality, and qualitative analysis of immunization staff interviews to gauge user perceptions. RESULTS: We conducted both studies between July and November 2012, with 628 (282 barcoded) vials processed for the APH study, and 749 (408 barcoded) vials for the study in FN communities. Barcode scanning led to significantly fewer immunization record errors than using drop-down menus (APH study: 0% vs. 1.7%; p=0.04) or typing in vaccine data (FN study: 0% vs. 5.6%; p<0.001). There was no significant difference in time to enter vaccine data between scanning and using drop-down menus (27.6s vs. 26.3s; p=0.39), but scanning was significantly faster than typing data into the record (30.3s vs. 41.3s; p<0.001). Seventeen immunization nurses were interviewed; all noted improved record accuracy with scanning, but the majority felt that a more sensitive scanner was needed to reduce the occasional failures to read the 2D barcodes on some vaccines. CONCLUSION: Entering vaccine data into immunization records through barcode scanning led to improved data quality, and was generally well received. Further work is needed to improve barcode readability, particularly for unit-dose vials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".