Culturally secure strategies for treating child chronic wet cough
Bibliographic record
Abstract
Background: Chronic wet cough (CWC) is highly prevalent among Indigenous children. It is often due to protracted bacterial bronchitis, which can lead to bronchiectasis if left untreated. Timely detection and management of CWC in primary care is crucial, but often lacking. In 2018, we implemented a program in a regional town in Australia which improved CWC outcomes through health promotion, clinician training, and practice changes. However, given Indigenous communities' cultural and geographical diversity, what was effective in one may not be elsewhere. We studied the barriers and facilitators to implementing the program in multiple communities. Method: An Indigenous co-led, participatory action research study with semi-structured interviews at seven Australian sites guided by the Consolidated Framework for Implementation Research. Data were analysed using NVivo. Results: 169 Indigenous family members and 95 health care practitioners (HCPs) participated. Families wanted culturally secure health information from Indigenous health staff delivered through tailored methods (home visits in small communities, events in larger ones), communication methods (spoken/visual) in local language and aligned with local culture. HCPs wanted regular best-practice-aligned training and diverse educational resources. Barriers included high staff turnover and intermittent doctor presence in remote areas. Desired practice changes included adding CWC to routine health screening, electronic prompts, and policies for non-medical HCPs to prescribe treatment. Conclusion: Strategies for timely detection and management of CWC are broadly consistent across contexts, but tailored approaches are essential due to diverse community characteristics.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".