Telepsychiatry and face-to-face psychiatric consultations during the first year of the COVID-19 pandemic in Australia: patients being heard and seen
Bibliographic record
Abstract
OBJECTIVE: The Australian federal government introduced additional Medicare Benefits Schedule (MBS) telehealth-items to facilitate care by private psychiatrists during the COVID-19 pandemic. METHOD: We analysed private psychiatrists' uptake of video and telephone-telehealth, as well as total (telehealth and face-to-face) consultations for April 2020-April 2021. We compare these to face-to-face consultations for April 2018-April 2019. MBS-Item service data were extracted for COVID-19-psychiatrist-video- and telephone-telehealth item numbers and compared with face-to-face consultations for the whole of Australia. RESULTS: Psychiatric consultation numbers (telehealth and face-to-face) were 13% higher during the first year of the pandemic compared with 2018-2019, with telehealth accounting for 40% of this total. Face-to-face consultations were 65% of the comparative number of 2018-2019 consultations. There was substantial usage of telehealth consultations during 2020-2021. The majority of telehealth involved short telephone consultations of ⩽15-30 min, while video was used more, in longer consultations. CONCLUSIONS: Private psychiatrists and patients continued using the new telehealth-items during 2020-2021. This compensated for decreases in face-to-face consultations and resulted in an overall increase in the total patient contacts compared to 2018-2019.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".