317 Using qualitative interviews to ascertain caregiver lived experiences when accessing post emergency department follow-up for children with headaches
Bibliographic record
Abstract
Objectives/Goals: To explore the caregivers’ lived experiences related to facilitators of and barriers to effective primary care or neurology follow-up for children discharged from the pediatric emergency department (PED) with headaches. Methods/Study Population: We used the descriptive phenomenology qualitative study design to ascertain caregivers’ lived experiences with making follow-up appointments after their child’s PED visit. We conducted semi-structured interviews with caregivers of children with headaches from 4 large urban PEDs over HIPAA-compliant Zoom conferencing platform. A facilitator/co-facilitator team (JH and SL) guided all interviews, and the audio of which was transcribed using the TRINT software. Conventional content analysis was performed by two coders (JH and AS) to generate new themes, and coding disputes were resolved by team members using Atlas TI (version 24). Results/Anticipated Results: We interviewed a total of 11 caregivers (9 mothers, 1 grandmother, and 1 father). Among interviewees, 45% identified as White non-Hispanic, 45% Hispanic, 9% as African-American, and 37% were publicly insured. Participants described similar experiences in obtaining follow-up care that included long waits to obtain neurology appointments. Participants also described opportunities to overcome wait times that included offering alternative healthcare provider types as well as telehealth options. Last, participants described desired action while awaiting neurology appointments such as obtaining testing and setting treatment plans. Discussion/Significance of Impact: Caregivers perceived time to appointment as too long and identified practical solutions to ease frustrations while waiting. Future research should explore sharing caregiver experiences with primary care providers, PED physicians, and neurologists while developing plans to implement caregiver-informed interventions.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".