Better Access: substantial shift to telehealth for allied mental health services during COVID-19 in Australia
Bibliographic record
Abstract
Objective To quantify the introduction of new, temporary telehealth Medicare Benefits Schedule (MBS) items delivered by allied mental health professionals (AMHPs) through the Better Access initiative during the COVID-19 pandemic in Australia. Methods MBS-item service data for clinical psychologists, registered psychologists, social workers, and occupational therapists were extracted for existing face-to-face, remote videoconferencing and new, temporary telehealth items for the study period April-December 2020. The total number of services in Australia were compared with the baseline period of 2019. Given the second wave of increased COVID-19 infections and prolonged lockdowns in the state of Victoria, we compared the per capita rate of services for Victoria versus other states and territories. Results During the study period, there was an overall 11% increase in all allied mental health consultations. Telehealth use was substantial with 37% of all sessions conducted by videoconferencing or telephone consultations. The peak month was April 2020, during the first wave of increasing COVID-19 cases, when 53% of consultations were via telehealth. In terms of Victoria, there was an overall 15% increase in all consultations compared with the same period in 2019. Conclusions Allied mental health services via MBS-subsidised telehealth items greatly increased during 2020. Telehealth is an effective, flexible option for receiving psychological care which should be made available beyond the pandemic. What is known about the topic? Little is known about the transition to and delivery of new, temporary Better Access telehealth services by AMHPs during the COVID-19 pandemic. What does this paper add? This paper provides valuable data on the rapid transition to telehealth by AMHPs to provide levels of psychological care commensurate to 2019. Data extends from April to December 2020 and includes the overall number of services provided for each profession, and the proportion of services delivered via face-to-face and telehealth. We highlight the impact of the new, additional items which temporarily raised the cap on sessions. We also illustrate the substantial use of the scheme by those living in Victoria who experienced greater COVID-19-related hardships. What are the implications for practitioners? The continuation of Better Access telehealth services by AMHPs has the potential to extend the reach of mental health care beyond the pandemic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".