Personal Opinions Seem to be the Major Contributor to the Influenza Vaccination Disparity in Sneedville, TN
Bibliographic record
Abstract
Relevance Although the seasonal flu vaccine remains the most effective way to prevent the spread of influenza and reduce its associated mortalities, the proportion of individuals receiving the vaccine continues to be an issue in various communities across the United States. The attitudes of residents who live in Sneedville, a small town in a rural northeastern Tennessee, were surveyed. Objective(s) To determine the barriers to influenza vaccination in Sneedville, Tennessee and contribute to the literature on why some rural communities across the United States show low influenza vaccination rates. Materials and Methods Door-to-door convenience sampling was conducted in Sneedville, TN. Participants were asked to complete a survey consisting of --three yes or no demographic questions (one with an option to further elaborate) and nine opinion questions based on a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree). Participants were not provided any additional details pertaining to the Likert scale questions and were given the option to skip the Likert scale questions. These questions were chosen to gauge the potential structural, socioeconomic, belief, and provider-related barriers to vaccination. Two-tailed independent t-tests were used to compare the Likert scale means for each of the nine opinion questions in those that received the influenza vaccine and those who did not. Univariate analysis was conducted to assess difference in the distribution of Likert responses in vaccinators compared to non-vaccinators. Results This project surveyed 172 residents of which 60.5% (104/172) indicated that they did not receive the influenza vaccine for the 2017-2018 flu season. Compared to individuals who vaccinate against the flu, individuals who do not vaccinate against the flu believe the flu shot is not worthwhile and believe the flu shot has a greater chance to make them sick. Conclusions This study finds that structural, socioeconomic, and provider-related barriers are not the underlying cause of the low influenza vaccination rates in this rural area. Instead, public opinion on influenza vaccination seems to be the reason for the disparity.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".