Perceptions of frontline staff regarding data collection methodologies used during the 2009 A H1N1 influenza immunization campaign in Canada
Bibliographic record
Abstract
BACKGROUND: During the 2009 H1N1 immunization campaign, electronic and hybrid (comprising both electronic and paper components) systems were employed to collect client-level vaccination data in clinics across Canada. Because different systems were used across the country, the 2009 immunization campaign offered an opportunity to study the usability of the various data collection methods. METHODS: A convenience sample of clinic staff working in public health agencies and hospitals in 9 provinces/territories across Canada completed a questionnaire in which they indicated their level of agreement with seven statements regarding the usability of the data collection system employed at their vaccination clinic. Questions included overall ease of use, effectiveness of the method utilized, efficiency at completing tasks, comfort using the method, ability to recover from mistakes, ease of learning the method and overall satisfaction with the method. A 5-point Likert-type scale was used to measure responses. RESULTS: Most respondents (96%) were employed in sites run by public health. Respondents included 186 nurses and 114 administrative staff, among whom 90% and 47%, respectively, used a paper-based method for data collection. Approximately half the respondents had a year or less of experience with immunization-related tasks during seasonal influenza campaigns. Over 90% of all frontline staff found their data collection method easy to use, perceived it to be effective in helping them complete their tasks, felt quick and comfortable using the method, and found the method easy to learn, regardless of whether a hybrid or electronic system was used. CONCLUSIONS: This study demonstrates that there may be a greater willingness of frontline immunization staff to adapt to new technologies than previously perceived by decision-makers. The public health community should recognize that usability may not be a barrier to implementing electronic methods for collecting individual-level immunization data.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".