Patient perspectives of pain mitigation strategies for adult vaccine injections
Bibliographic record
Abstract
Aims:To evaluate an educational pamphlet that incorporates evidence-based pain mitigation strategies during adult vaccine injections and determine its effect on the knowledge, attitudes and behaviours toward use of such strategies among adults in the community receiving immunizations.Methods:An evidence-based pamphlet about how to reduce pain during vaccination in adults was distributed to a convenience sample of community sites that administer vaccines, including family physician offices, travel clinics, and pharmacies. Providers at the community sites distributed a baseline (pre) questionnaire followed by the pamphlet to study participants. Then participants were vaccinated. Six weeks later, participants were contacted to complete a follow-up (post) questionnaire. Participants’ knowledge, attitudes and behaviours regarding pain mitigating strategies for vaccine injections were evaluated before and after access to the pamphlet.Results:Seventy-four people receiving vaccines participated. Participants were predominantly university educated (69%) and female (66%), with a median age of 44.5 years (range 18 - 71). Most participants received an injection at a travel or public health clinic (73%). Twenty-seven percent had prior accurate knowledge of pain mitigation strategies. Self-reported pain or fear of needle pain did not change from before access to the pamphlet to six weeks after. Twenty percent of participants used at least one strategy outlined in the pamphlet and found it helpful and 52% were interested in sharing the pamphlet with others.Conclusions:An educational pamphlet about vaccination pain mitigation resulted in a positive change in knowledge and attitudes around pain mitigation strategies. Further research is needed to explore long-term impact.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".