Impact of the COVID-19 Pandemic on Manual Therapy Service Utilization within the Australian Private Healthcare Setting
Bibliographic record
Abstract
The COVID-19 pandemic has impacted a wide range of health services. This study aimed to quantify the impact of the COVID-19 pandemic on manual therapy service utilization within the Australian private healthcare setting during the first half of 2020. Quarterly data regarding the number and total cost of services provided were extracted for each manual therapy profession (i.e., chiropractic, osteopathy, and physiotherapy) for the period January 2015 to June 2020 from the Australian Prudential Regulation Authority. Time series forecasting methods were used to estimate absolute and relative differences between the forecasted and observed values of service utilization. An estimated 1.3 million (13.2%) fewer manual therapy services, with a total cost of AUD 84 million, were provided within the Australian private healthcare setting during the first half of 2020. Reduction in service utilization was considerably larger in the second quarter (21.7%) than in the first quarter (5.7%), and was larger in physiotherapy (20.6%) and osteopathy (12.7%) than in chiropractic (5.2%). The impact varied across states and territories, with the largest reductions in service utilization observed in New South Wales (17.5%), Australian Capital Territory (16.3%), and Victoria (16.2%). The COVID-19 pandemic has had a profound impact on manual therapy service utilization in Australia. The magnitude of the decline in service utilization varied considerably across professions and locations. The long-term consequences of this decline in manual therapy utilization remain to be determined.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".