Multi-lingual “Asthma APP” improves health knowledge of asthma among Australian First Nations carers of children with asthma
Bibliographic record
Abstract
Background Among Australian First Nations people, asthma is associated with worse morbidity and mortality than non-First Nations people. Improving the delivery of health education that is innovative and culturally relevant to linguistically diverse populations is needed. Digital platforms, such as mobile applications (APP), have the potential to improve evidence-based health education, particularly in settings where access to specialist services is limited and turnover of staff is high, such as in remote Australia. In response to consumer needs, we developed a multi-lingual Asthma APP from our existing asthma flipchart, with a “voice-over” in seven local First Nations languages and English, using a mixture of static and interactive formats. In this study, we evaluated (a) the functionality and usability of the APP with First Nations health professionals with and without asthma and (b) whether the APP improves health knowledge and understanding of asthma among First Nations carers of children with asthma. Methods In total, 7 First Nations health professionals participated in semi-structured interviews prior to the evaluation with 80 First Nations carers of children with asthma from the Northern Territory and Queensland, Australia. Carers underwent pre- and post-education questionnaires (maximum score = 25), where the post-questionnaire was administered immediately post the APP education session. Results Health professionals found that APP was easy to navigate and culturally appropriate. Among the 80 carers, most were mothers (86%), aged between 26 and 50 years (75%) and 61% lived in remote settings (>100 km from a tertiary hospital). Most carers chose English audio (76%) with the remainder choosing one of the First Nations languages. Overall, asthma knowledge significantly improved post-education (median scores pre = 21 [interquartile range (IQR), 19–22; post = 24 (IQR 22–24), p = 0.05]. Conclusion The First Nations-specific multi-lingual Asthma APP was easy to use and acceptable for the use by health professionals that also significantly improved short-term asthma knowledge among First Nations carers of children with asthma. The Asthma APP is an innovative and culturally acceptable method of delivering evidence-based, health education to culturally and linguistically diverse populations among Australian First Nations people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".