Policy Implications for Controlling Communicable Diseases in Indigenous Communities: Case of Strongyloidiasis in Australia
Bibliographic record
Abstract
The objective of this paper is to document the knowledge and experiences of healthcare professionals and researchers in Australia about the barriers to controlling Strongyloides stercoralis in Australian Indigenous communities. Qualitative research methods were used to conduct in-depth semi-structured interviews, which were digitally recorded, transcribed, and participant-checked. Data were thematically analysed to identify significant themes. Five major themes were identified:1) Barriers to health/treatment;2) Access to healthcare;3) Policy;4) Learning opportunity; and5) Ideas for intervention.The findings suggest that Australian Indigenous communities will continue to suffer increased morbidity and mortality due to a lack of control or prevention of Strongyloides stercoralis. Issues such as institutional racism, improvements to health promotion, education, socioeconomic determinants, and health care system policy and procedures need to be addressed. This study identifies several direct implications for Indigenous health:The need for increased knowledge and understanding of the risks to health for Indigenous community members;The need for prevention policy development for neglected tropical diseases in Indigenous communities;The need for increased knowledge and understanding of the treatment, diagnosis, and healthcare access concerning Strongyloides stercoralis for health professionals and policymakers who work within Indigenous health;The need to raise awareness of systematic institutional racism in the control and prevention of neglected tropical diseases in Indigenous communities; andThe need for a health promotion framework that can provide the basis for multiple-level interventions to control and prevent Strongyloides in Indigenous communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".