Addressing Parental Concerns About Pain During Childhood Vaccination
Bibliographic record
Abstract
OBJECTIVE: Pain from vaccine injections remains undertreated, despite the availability of numerous pain-relieving strategies. Healthcare providers report lack of time within current office workflows as a major barrier to routine pain management. The objective was to document the total time involved in outpatient vaccine appointments to test the hypothesis that offering pain-relieving strategies can be practically implemented when considering the element of time to vaccine injection. PATIENTS AND METHODS: Prospective naturalistic study in 8 urban outpatient primary care clinics (4 pediatric and 4 family practice) in Toronto. For 48 to 59 consecutive childhood vaccination appointments at each site, child waiting time from clinic arrival until first vaccine injection was tracked. RESULTS: Altogether, 405 vaccine appointments were included. The median age of the child undergoing vaccination was 12 months. The mean (SD) time from clinic arrival until first vaccine injection was 41.6 minutes (20.9), with a range of 7 to 132 minutes. Linear regression identified a significant (P<0.05) difference according to clinic [ranging from 19.4 min (6.5) to 57.5 min (20.2)] and number of family members in the appointment [ranging from 40.6 min (21.0) for an appointment in the index child only to 50 min (14.3) for an appointment in the index child and 2 other family members]. CONCLUSIONS: Contrary to healthcare provider perceptions, the timing of outpatient childhood vaccine appointments allows for the inclusion of pain management interventions. Efforts should now focus on educating healthcare providers and parents about the value of pain management and how to implement evidence-based strategies.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".