Australian private practice metropolitan telepsychiatry during the COVID-19 pandemic: analysis of Quarter-2, 2020 usage of new MBS-telehealth item psychiatrist services
Bibliographic record
Abstract
OBJECTIVE: The Australian Commonwealth Government introduced new psychiatrist Medicare-Benefits-Schedule (MBS)-telehealth items in the first wave of the COVID-19 pandemic to assist with previously office-based psychiatric practice. We investigate private psychiatrists' uptake of (1) video- and telephone-telehealth consultations for Quarter-2 (April-June) of 2020 and (2) total telehealth and face-to-face consultations in Quarter-2, 2020 in comparison to Quarter-2, 2019 for Australia. METHODS: MBS item service data were extracted for COVID-19-psychiatrist-video- and telephone-telehealth item numbers and compared with a baseline of the Quarter-2, 2019 (April-June 2019) of face-to-face consultations for the whole of Australia. RESULTS: Combined telehealth and face-to-face psychiatry consultations rose during the first wave of the pandemic in Quarter-2, 2020 by 14% compared to Quarter-2, 2019 and telehealth was approximately half of this total. Face-to-face consultations in 2020 comprised only 56% of the comparative Quarter-2, 2019 consultations. Most telehealth provision was by telephone for short consultations of ⩽15-30 min. Video consultations comprised 38% of the total telehealth provision (for new patient assessments and longer consultations). CONCLUSIONS: There has been a flexible, rapid response to patient demand by private psychiatrists using the new COVID-19-MBS-telehealth items for Quarter-2, 2020, and in the context of decreased face-to-face consultations, ongoing telehealth is essential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".