Evaluation of a pilot project to increase influenza vaccine coverage in patient with chronic diseases
Bibliographic record
Abstract
Abstract Background Chronic diseases are a major risk factor for influenza morbidity and mortality. However, influenza vaccine coverage in people with chronic diseases remains low, around 43%, in Quebec, Canada. Various strategies are being tested to improve the vaccination rates for this population. A pilot project was implemented in a rheumatology outpatient clinic during the 2018-19 season to improve vaccination access in this often-immunosuppressed group of patients. All patients having an appointment at the clinic during the vaccination period were systematically invited to see the nurse and offered vaccination if they met the program criteria, namely if they were taking immunosuppressive drugs. Methods Implementation and results of the project were evaluated using mixed methods. Data on vaccination were collected from the nurses' forms and from a patient self-administered questionnaire. Data on implementation was collected through the patient questionnaire and semi-directed interviews with the clinic physicians, the clinic nurse and managers. Results A total of 1135 patients were evaluated by the nurses during the project and 427 completed the patient questionnaire. Total vaccination rate amongst patients seen by the nurses was 63%. Based on patient questionnaires results, vaccination was increased by 46%, as compared to the previous year (52% in 2017-18 vs 76% in 2018-19). The project was well received. Key elements of its success were integration in regular clinic activities, support for the initiative by patients and professionals and some logistic aspects such as preloaded syringes. Barriers were mostly related to excess workload and vaccine management. Conclusions Overall, the project improved vaccination coverage and was considered a success. Lessons learned were used to adjust and spread this initiative to more outpatient clinics using personnel dedicated for vaccination rather than using the clinic nurse. A phase II project was done and evaluated in 2019-2020. Key messages Increasing timely access to vaccination helps to increase influenza vaccine coverage. Managers should plan for the increased workload on clinical and clerical personnel when implementing systematic vaccination offer.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".