Addressing vaccine hesitancy: Clinical guidance for primary care physicians working with parents.
Bibliographic record
Abstract
OBJECTIVE: To provide primary care physicians with clinical guidance for addressing parental vaccine hesitancy. SOURCES OF INFORMATION: . References of identified articles were assessed for additional relevant articles. A separate gray literature search was conducted using Google to find best-practice guidelines from public health and health care organizations, knowledge translation materials for health care providers, and resources that could be used in discussions with parents about vaccines. MAIN MESSAGE: Practical tips for addressing parental vaccine hesitancy in primary care include starting early, presenting vaccination as the default approach, building trust, being honest about side effects, providing reassurance on a robust vaccine safety system, focusing on protection of the child and community, telling stories, and addressing pain. Also provided are statements that providers could use in vaccination-related conversations; answers to commonly asked questions on benefits, safety, and immunologic aspects of vaccines; and links to a number of online resources for physicians and parents. CONCLUSION: Vaccine-hesitant parents who are on the fence far outnumber vaccine refusers; therefore, counseling this group might be more effective. Reasons behind vaccine hesitancy are complex and encompass more than just a knowledge deficit. As a trusted source of information on vaccines, family physicians play a key role in driving vaccine acceptance.
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".