Mālama nā makua i nā keiki me ka hānō: Native Hawaiian Parents Caring for Their Children with Asthma, (Part 2)
Bibliographic record
Abstract
Objective: Native Hawaiian children have the highest prevalence of asthma among all ethnicities in Hawai'i.Mālama Part 2 describes continuing research, exploring contemporary native Hawaiian parents' perspective, and experience of caring for their children with asthma in the context of uncertainty while living on the islands of Hawaiʻi, Kauaʻi, Maui, Molokaʻi and Lānaʻi.Design: Descriptive qualitative approach by means of directed content analysis using focus groups was applied to this study.Eight open-ended questions elicited asthma history, asthma management, and how the Hawaiian culture affects parents' health practices.Directed content analysis applied Mishel's Uncertainty in Illness Theory (UIT) to guide data collection, organization, and analysis.Sample: Thirtythree native Hawaiian parents with a child with asthma met in 9 separate focus groups during 2012-2015 on the islands of Hawaiʻi, Kauaʻi, Maui, Molokaʻi, and Lānaʻi.Results: The study's findings were congruent with the first Mālama study results of focus groups on Oʻahu.Contextual influences including indigenous worldview, cultural values, history, and assimilation and acculturation factors affected native Hawaiian parents' perceptions and experiences with conventional asthma care.Moreover, Hawaiian parents living on islands outside of metropolitan Oʻahu reported geographic barriers that contributed to their uncertainty.Conclusion: Political action is required for comprehensive medical care, health education, and nursing services to be delivered to families living on all islands.Integrating Hawaiian cultural values, involving 'ohana, and applying complementary alternative therapies as well as standard asthma management will strongly support native Hawaiian parents caring for their 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".