Children’s Perspectives on Outpatient Physician Visits: Capturing a Missing Voice in Patient-Centered Care
Bibliographic record
Abstract
Actively involving children in their healthcare is a core value of patient-centered care. This is the first study to directly obtain children’s detailed perspectives on positive and negative aspects of outpatient physician visits in a primary care setting (e.g., checkups) and their preferred level of participation. Individual interviews were conducted with 167 children (female n = 82, male n = 85; ages 7–10, Mage = 8.07 years, SD = 0.82). Open-ended questions were used so that children’s responses were not confined to researchers’ assumptions, followed by close-ended questions to meet specific objectives. Quantitative content analysis, correlations, logistic regression, and Cochran’s Q were used to explore the data. Children were highly fearful of needle procedures (61%), blood draws (73%), pain (45%), and the unknown (21%). Children indicated that they liked receiving rewards (32%) and improving their health (16%). Children who were more fearful during physician visits wanted more preparatory information (ExpB = 1.05, Waldx2(1) = 9.11, p = 0.003, McFadden’s R22 = 0.07) and more participation during the visit (ExpB = 1.04, Waldx2(1) = 5.88, p = 0.015, McFadden’s R22 = 0.03). Our results can inform efforts to promote positive physician visit experiences for children, reduce procedural distress, and foster children’s ability to take an active role in managing their health.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".