Assessing the Knowledge of Anaphylaxis Management and Adrenaline Auto-Injector Administration among Parents of Children with Food Allergies: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Despite the importance of caregivers being trained in anaphylaxis management and the use of adrenaline auto-injectors (AAIs), studies have revealed inadequate caregiver knowledge. The caregiver anaphylaxis knowledge in an Irish population has not been previously assessed. This study aims to evaluate the anaphylaxis management knowledge and AAI administration proficiency among parents of children with food allergies. Methods: The parents of children with food allergies who were prescribed an AAI were invited to take part in a study involving online education. The participants completed an online questionnaire assessing anaphylaxis knowledge. They then took part in an online educational intervention where their AAI administration ability was assessed. Results: Out of a total score of 12, the mean anaphylaxis knowledge score was 9.76/12, SD 1.577, or 81.33%. Of the 152 participants, 26.7% (n = 40) performed all three critical AAI administration steps correctly. A household income under EUR 40,000 per annum reduced the likelihood of successful AAI administration (OR 0.33 95% CI 0.125–0.87, p = 0.025). Regarding the AAI devices, 46.4% claimed to have switched between devices at least once before. Conclusions: The parents demonstrated good knowledge of anaphylaxis management, but the prevalence of device switching underscores the importance of comprehensive AAI training. Future assessments should include evaluations of enhancements in knowledge, anxiety levels, and overall quality of life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".