Increased Australian outpatient private practice psychiatric care during the COVID-19 pandemic: usage of new MBS-telehealth item and face-to-face psychiatrist office-based services in Quarter 3, 2020
Bibliographic record
Abstract
OBJECTIVE: The Australian federal government introduced new COVID-19 psychiatrist Medicare Benefits Schedule (MBS) telehealth items to assist with providing private specialist care. We investigate private psychiatrists' uptake of video and telephone telehealth, as well as total (telehealth and face-to-face) consultations for Quarter 3 (July-September), 2020. We compare these to the same quarter in 2019. METHOD: MBS-item service data were extracted for COVID-19-psychiatrist video and telephone telehealth item numbers and compared with Quarter 3 (July-September), 2019, of face-to-face consultations for the whole of Australia. RESULTS: The number of psychiatry consultations (telehealth and face-to-face) rose during the first wave of the pandemic in Quarter 3, 2020, by 14% compared to Quarter 3, 2019, with telehealth 43% of this total. Face-to-face consultations in Quarter 3, 2020 were only 64% of the comparative number of Quarter 3, 2019 consultations. Most telehealth involved short telephone consultations of ⩽15-30 min. Video consultations comprised 42% of total telehealth provision: these were for new patient assessments and longer consultations. These figures represent increased face-to-face consultation compared to Quarter 2, 2020, with substantial maintenance of telehealth consultations. CONCLUSIONS: Private psychiatrists continued using the new COVID-19 MBS telehealth items for Quarter 3, 2020 to increase the number of patient care contacts in the context of decreased face-to-face consultations compared to 2019, but increased face-to-face consultations compared to Quarter 2, 2020.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".