Interpersonal comparison among caregivers of children with asthma
Bibliographic record
Abstract
Objective: We examined the extent to which caregivers of children with asthma used interpersonal comparisons—a novel comparison process that parallels social comparison and temporal comparison—to form judgments about their child. Methods & Measures: Using semi-structured interviews adapted from the McGill Illness Narrative Interview, we examined the interpersonal comparisons that caregivers of a child with asthma (n = 41) made regarding their child. Results: Interpersonal comparisons influenced caregiver thoughts, feelings, and behavior. They helped caregivers distinguish asthma from other breathing problems, evaluate the severity of the asthma, and understand their child’s experience. However, they also created uncertainty by highlighting the complex, unpredictable nature of asthma. Interpersonal comparisons were a source of gratitude and hope, but also worry and frustration. Finally, interpersonal comparisons influenced caregivers’ decisions and actions, resulting in decisions that aligned with and, at times, ran counter to biomedical models of asthma care. In some instances, caregivers used interpersonal comparisons to motivate their child’s behavior. Conclusion: The interpersonal comparisons served as a source of information for caregivers trying to understand and manage their child’s asthma. Investigating these comparisons also expands how we think about other comparison theories.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".