Seasonal Variations of Australian Medicare-Reimbursed Psychiatric Consultations Between 2016 and 2023: A Time Series Analysis
Bibliographic record
Abstract
Objective: In Australia, subsidized psychiatric consultation items in the Medicare Benefits Schedule (MBS) provide essential private psychiatric services. Seasonality in service utilization may affect health care planning. This study examined the seasonal patterns of overall MBS psychiatric consultations and MBS telehealth psychiatric consultations in pre-and postpandemic periods. Methods: Medicare Item Reports for face to-face and telehealth psychiatric items from 2016 to 2023 were retrieved and compiled. The quarterly time series for total (face-to-face and telehealth) and telehealth psychiatric consultations were analyzed descriptively, using the January–March quarter as the baseline. Linear regression analyses were performed to detect significant seasonal variations by gender and age groups. A sensitivity analysis of the impact of the post–COVID-19 increase in consultations on seasonality was also conducted. Results: A seasonal pattern was present for total consultations before and after the expansion of telehealth items in the first quarter of 2020. There were peaks in psychiatric consultations in July–September and troughs in January–March, except in patients ≥65 years old. Total consultations were significantly higher in April–June (P =.010) and July–September (P.001) than in January–March. Seasonal variations were the largest among young patients aged 0–24 years. Seasonality was mostly unaffected by the increase in psychiatric consultations postpandemic. However, seasonality was absent for telehealth consultations. Conclusion: The seasonality of MBS psychiatric consultations, which was more prominent in young people, may have a practical impact on psychiatric service planning. The lack of seasonal variation in telehealth consultations and its relationship to emergency presentations warrant further research.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".