Helping healthcare workers decide: evaluation of an influenza immunization decision tool.
Bibliographic record
Abstract
Healthcare workers (HCW) experience decisional conflict or uncertainty of the best alternative when deciding about influenza immunization. Despite free and easy access to influenza vaccine, and resource consuming campaigns, immunization rates among HCW remain unacceptably low. This is in part due to decisional conflict, which may be alleviated by a decision aid. To address this issue we developed the Ottawa Influenza Decision Aid (OIDA) to help HCW make an informed decision about influenza immunization. The OIDA was tested in a large acute care hospital during the influenza immunization campaign. We recruited HCWs from the Orthopaedic Ward and Logistical Services, using block randomization, to complete the OIDA and a feedback questionnaire. The majority (85%) of respondents that completed the OIDA felt that immunization was very important to avoid getting influenza and 95% were sure of the best choice for them. In response to the feedback questionnaire, 84% of respondents found the information clear and 77% concluded the OIDA helped them to recognize a decision. Results of this study support the OIDA as a useful tool for HCWs considering influenza immunization. This study is an important step towards evaluating the usefulness of the OIDA within prevention campaigns. Recommendations include evaluation of the OIDA by incorporating it into large-scale influenza immunization campaigns.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".