Benchmarking public health pain management practices during school immunizations
Bibliographic record
Abstract
BACKGROUND: Pain and fear during immunizations can affect children and their future behaviour toward immunization. These negative experiences can be amplified when children receive vaccines as part of school-based immunization programs, where parental or tutor supports are missing. In 2015, HELPinKIDS&ADULTS, a Canadian network of experts, published a clinical practice guideline (CPG) on the management of pain and fear during immunization. This guideline has been endorsed by international, national and provincial organizations. However, the level of integration and implementation of the CPG into local and community immunization programs such as school-based immunization clinics is unclear. METHODS: An investigation whether public health units in Ontario integrated and implemented the pain and fear interventions recommended by the CPG into school-based immunization policies and practices was concluded. RESULTS: The study shows that the majority of public health units do have pain and fear policies and procedures in place, but interventions are not integrated in a consistent and formal manner, leading to suboptimal uptake of interventions during immunizations at school. CONCLUSION: For pain interventions to be applied with sufficient fidelity and in enough individuals to have a meaningful effect, organizational leaders need to create directives and procedures that support implementation in a systematic and accountable manner.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".