Situating household management of children’s asthma in the context of social, economic, and environmental injustice
Bibliographic record
Abstract
BACKGROUND: Structural determinants of health are social, economic, and environmental forces that generate unequal opportunities for resources and unequally distribute exposure to risk. For example, economic constraint, racial discrimination and segregation, and environmental injustice shape population-level asthma prevalence and severity. Structural determinants are especially relevant to consider in clinical settings because they affect everyday household asthma management. OBJECTIVE: To examine how structural determinants shape everyday household management of pediatric asthma and offer a framework for providers to understand asthma management in social context. DESIGN: Qualitative interviews of caregivers for children with asthma. PARTICIPANTS: = 16). Most caregivers were women (83%), Black (73%) and/or had low socioeconomic status (SES; 78%). Caregivers cared for children with asthma aged 0-4 (32%), 5-11 (68%) and 12-17 (54%). APPROACH: We carried out narrative interviews with caregivers using an adapted McGill Illness Narrative Interview and using qualitative analysis techniques (e.g. inductive and deductive coding, constant comparison). KEY RESULTS: Caregivers highlighted three ways that structural determinants complicated asthma management at home: 1) housing situations, 2) competing household illnesses and issues, and 3) multi-household care. CONCLUSIONS: By connecting social, economic, and environmental injustices to the everyday circumstances of asthma management, our study can help providers understand how social contexts challenge asthma management and can open conversations about barriers to adherence and strategies for supporting asthma management at home. We offer recommendations for medical system reform, clinical interactions, and policy advocacy.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".