A rapid review of evidence to inform an ear, nose and throat service delivery model in remote Australia
Bibliographic record
Abstract
INTRODUCTION: This rapid literature review aimed to inform the development of a new sustainable, evidence-based service delivery model for ear, nose and throat (ENT) services across Cape York, Australia. This work seeks to investigate the research question: 'What are the characteristics of successful outreach services which can be applied to remote living Indigenous children?' METHODS: A comprehensive search of three major electronic databases (PubMed, CINAHL and MEDLINE) and two websites (HealthInfo Net and Google Scholar) was conducted for peer-reviewed and grey literature, to elicit characteristics of ENT and hearing services in rural and remote Australia, Canada, New Zealand and the USA. The search strategy was divided into four sections: outreach services for rural and remote communities; services for Indigenous children and families; telehealth service provision; and remote ear and hearing health service models. A narrative synthesis was used to summarise the key features of the identified service characteristics. RESULTS: In total, 71 studies met the inclusion criteria and were included in the review, which identified a number of success and sustainability traits, including employment of a dedicated ear and hearing educator; outreach nursing and audiology services; and telehealth access to ENT services. Ideally, outreach organisations should partner with local services that employ local Indigenous health workers to provide ongoing ear health services in community between outreach visits. CONCLUSION: The evidence suggests that sound and sustainable ENT outreach models build on existing services; are tailored to local needs; promote cross-agency collaboration; use telehealth; and promote ongoing education of the local workforce.
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 | 0.037 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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".