The Impact of COVID-19 on First Nations People Health Assessments in Australia
Bibliographic record
Abstract
The COVID-19 (coronavirus disease 2019) pandemic has the potential to worsen existing health inequalities faced by Aboriginal and Torres Strait Islander peoples in Australia. We aimed to assess the impact of the pandemic on First Nations people health assessments using an interrupted time series model utilizing data extracted from the Australian Medicare Benefits Schedule database. Additive triple exponential smoothing was used to model health assessments undertaken between January 2017 and December 2019. The model was used to predict health assessments between January 2020 and June 2020 with 95% confidence ( P < .05). There was no significant difference between observed and predicted First Nations people health assessments in January, February, and June 2020. However, we found a statistically significant decrease in health assessments in March (16.5%), April (23.1%), and May (17.2%) 2020. The proportion of total health assessments delivered via telehealth was 0.5%, 23.6%, 17.6%, and 10.0% for March, April, May, and June 2020, respectively. The decrease in total First Nations people health assessments compounds the risk of poorer health outcomes in this population already vulnerable due to a high burden of chronic disease and considerable social, economic, and health inequalities. Strategies to improve the delivery of telehealth for First Nations people must be considered.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".