Bibliographic record
Abstract
PURPOSE: To explore the experience of dyspnea in school-age children with asthma including exploring children's perceptions of the (1) sensations of dyspnea, (2) precipitants of dyspnea, (3) coping strategies used to deal with dyspnea, and (4) effects of dyspnea on lives of children. STUDY DESIGN AND METHODS: This interpretive, descriptive, qualitative research study had a sample of 30 school-age children diagnosed with asthma. Data collection involved individual open-ended interviews combined with drawings. Transcribed data were analyzed using the constant comparative method. RESULTS: The childrens' experiences with dyspnea were represented by five themes: (1) it is an overwhelming feeling, (2) it is mainly..., (3) I slow it down, (4) others only need to help when it is really bad, and (5) I am not a player. Although children varied with respect to how they described their experiences, they all reinforced that the sensation of dyspnea was distressing and painful, something that when experienced overshadowed everything else. CLINICAL IMPLICATIONS: Children with dyspnea have much to share about what it is like to experience dyspnea that may be used by nurses to provide comprehensive and sensitive care. Nurses need to take into account the individuality of children's dyspnea experiences when developing treatment plans for children with asthma. Education programs that are tailored to meet individual needs will help children to take control and manage their dyspnea.
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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 | 0.002 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".