Managing Children with Asthma at Home Daycares: The Views of Providers
Bibliographic record
Abstract
Background: Over 180,000 deaths annually are caused by asthma. It is expected that more than 80% of these deaths could be avoided with proper educational and chronic disease management programs. For example, Rayne Stanley, a three-year-old child, died at a daycare center in the United States after suffering from severe asthma. Similarly, Ryan Gibbons, a young boy from southwestern Ontario died in October 2012 after suffering from an asthma attack at school. Rayne Stanley and Ryan Gibbons are two examples of deaths that could have been avoided with the right asthma management programs. The limited literature in this field shows that asthma management programs are not implemented well at daycares and preschools. \n \nAim: The purpose of this study was to explore how home daycare staff manages asthma for children under five years. It explored how asthma management is understood by home daycare providers, and how home daycare providers monitor children with asthma. It also explored potential barriers and facilitators to asthma management at home daycares. \n \nMethods: This research was designed as qualitative, exploratory research. Semi-structured individual face to face interviews with eleven home daycare providers (n=11), which included both licensed and unlicensed home daycares operating in the Region of Waterloo in Ontario, Canada. All interviews were tape-recorded and transcribed verbatim. Thematic analysis was used to analyze transcripts for common themes using the Framework Approach. \n \nResults: Three themes emerged in the analysis: a) Knowledge about diagnosing asthma, b) dealing with asthma attacks, and c) asthma management. The results suggested that (a) daycare provider’s experience in dealing with childhood asthma plays a major role in asthma management practices, where providers with their own children suffering from asthma provided a great model for asthma management in daycares. However, less experienced providers, who represent forty-five percent of participants in this study, fell short in many areas. This gap was enabled by the (b) scarcity of legislations and training programs for childhood asthma management in daycares. The gap was widened by the (c) lack of access to child asthma related medical information. \n \nConclusions: In this section, the findings are presented in the context of expected impact areas. The findings showed that home daycare providers managing children with asthma in this study indicated the need for (a) asthma management training, (b) establishing official processes and policies for asthma management, and (c) interventions to improve access to and communication with healthcare systems to enable asthma related information acquisition. The recommendations in this section help to establish a well-informed, more proactive, home daycare provider network and a safer environment for children with asthma.
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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 | 0.008 | 0.017 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".