Greatly increased Victorian outpatient private psychiatric care during the COVID-19 pandemic: new MBS-telehealth-item and face-to-face psychiatrist office-based services from April–September 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 telehealth, and face-to-face consultations for April-September 2020 for the state of Victoria, which experienced two consecutive waves of COVID-19. We compare these to the same 6 months in 2019. METHOD: MBS-item-consultation data were extracted for video, telephone and face-to-face consultations with a psychiatrist for April-September 2020 and compared to face-to-face consultations in the same period of 2019 Victoria-wide, and for all of Australia. RESULTS: Total Victorian psychiatry consultations (telehealth and face-to-face) rose by 19% in April-September 2020 compared to 2019, with telehealth comprising 73% of this total. Victoria's increase in total psychiatry consultations was 5% higher than the all-Australian increase. Face-to-face consultations in April-September 2020 were only 46% of the comparative 2019 consultations. Consultations of less than 15 min duration (87% telephone and 13% video) tripled in April-September 2020, compared to the same period last year. Video consultations comprised 41% of total telehealth provision: these were used mainly for new patient assessments and longer consultations. CONCLUSIONS: During the pandemic, Victorian private psychiatrists used COVID-19-MBS-telehealth-items to substantially increase the number of total patient care consultations for 2020 compared to 2019.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".