1662. Perspectives of Caregivers of Children with Community-Acquired Pneumonia in the Emergency Department: A Qualitative Study
Bibliographic record
Abstract
Abstract Background Despite the fact that paediatric community-acquired pneumonia (CAP) is usually caused by viruses, antibiotics are often prescribed. The objective of this study was to understand caregiver perspectives and experiences relating to the treatment of paediatric CAP. Methods This was a phenomenologically-informed qualitative study involving interviews with caregivers of young children in Hamilton, Ontario. Caregivers were interviewed with open-ended questions relating to germ theory, the nature of pneumonia, and the role of antibiotic treatment. The principles of conventional content analysis were used to guide the coding and synthesis of the transcribed interviews. Results Eleven caregivers were interviewed. Most had a good understanding of the difference between bacteria and viruses and many knew that antibiotics were not effective against all types of infections. Many also stated that there was an increased risk of developing resistance with frequent antibiotic use. However, there were misconceptions that probiotics effectively mitigated antibiotic side effects and few were familiar with the potential long-term impacts of antibiotic use in children. There was variability in the perceived severity of paediatric CAP. For children sick enough to require treatment in the emergency department (ED), some participants thought that antibiotic treatment would be important to prescribe to accelerate recovery, as well as to prevent caregivers from feeling helpless. However, participants also thought it was inappropriate for physicians to prescribe antibiotics solely to make the caregiver feel better. Many caregivers also felt strongly that clinical follow-up and discussions on treatment risks and benefits would be desirable and might counteract feelings of helplessness that might result from being sent home without a prescription. Conclusion Recognizing that parents may have misperceptions about antibiotic use for CAP (and may seek antibiotics without strong rationale) can inform clinicians’ efforts to better educate and support caregivers in the ED. Care strategies informed by caregiver experiences can improve parent-provider communication, increase trust in the attending clinician, and reduce antibiotic misuse. Disclosures Jeffrey Pernica, MD, MSc, FRCPC, DTMH, MedImmune: Grant/Research Support|Merck: Grant/Research Support
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".