Parents' self-reported experiences and information needs related to acute pediatric asthma exacerbations: A mixed studies systematic review
Bibliographic record
Abstract
Objective: To systematically review the scientific literature examining parents' experiences and information needs for the management of their child's asthma exacerbations. Methods: We searched five databases for quantitative and qualitative studies in Canada and the United States from 2002 onwards. A convergent integrated approach and the Mixed Method Appraisal Tool were used to analyze and appraise the evidence, respectively. Results: We included 84 studies (27 quantitative, 54 qualitative, 3 mixed methods). Some parents lacked confidence in recognizing or managing exacerbations. A few parents were uncertain when and where to seek medical help. The main barrier to accessing care was cost. Impacts on parents included poor sleep, distress, and lifestyle disruptions. Parents felt they lacked information and wanted education on treatments and how to recognize and manage exacerbations via education sessions, written materials, community outreach and online resources. Conclusion: Improved education for parents may help reduce parents' stress, asthma-related morbidities for children and use of urgent health services. Innovation: The development of tailored interventions and knowledge translation strategies with input from target audiences (e.g. parents, health care providers) is necessary to meet their information needs and support adherence to clinical recommendations.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".