Factors Influencing Influenza Vaccination Rates Among Rural Ontario Paramedics
Bibliographic record
Abstract
Introduction. Influenza vaccination rates have traditionally been very low among healthcare workers (HCWs) however; very few studies have examined vaccination rates andmotivators among paramedics.\nObjectives. The Health Belief Model (HBM) (Rosenstock,1974) was used as a guideline in this study to better understand the motivators and barriers to flu vaccination among rural Ontario paramedics. This group represents a considerable proportion of the HCW community, yet it has been virtually omitted from previous research.\nMethods. Through the use of self-report questionnaires, and using the HBM as a guideline, a graphical representation of the decision-making process regarding flu vaccination was generated. The sample included 99 independent responses received from 5 rural Ontario Emergency Medical Services (EMS): Bruce County EMS; County of Renfrew Paramedic Services; Haldimand County EMS; Haliburton County (Muskoka) EMS; and Perth County EMS. Univariate, Bivariate and Logistic Regression Analyses were conducted to evaluate data.\nResults. Living arrangement (OR=4.80, 95%CI: 1.13-20.46) was found to directly affect vaccination rates within this group. Male gender (OR=2.50, 95%CI: 0.62-10.05), less than 5 years of service (OR=5.00, 95%CI: 0.54-46.72) and more than 20 years of service (OR=5.50, 95%CI: 0.59-51.19) trended toward higher rates of vaccination. There was no effect of age or level of education. Increased convenience has been previously cited as a way to improve vaccination rates, however; it appeared only to assist in improving rates for individuals already considering vaccination.\nConclusions. Increased Potential Benefits and Cues to Action are two dimensions of the HBM that could affect a change in vaccination status. This increased knowledge is useful in the development of targeted vaccine uptake initiatives that could lead to increased rates of vaccination among paramedics, HCWs and the community at large.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".