A Snapshot of the Counselling and Psychotherapy Workforce in Australia in 2020: Underutilised and Poorly Remunerated, Yet Highly Qualified and Desperately Needed
Bibliographic record
Abstract
The aim of the 2020 workforce survey was to profile professionals affiliated with the Psychotherapy and Counselling Federation of Australia (PACFA) to inform future policy and service planning. PACFA is a national peak body for Australian counsellors and psychotherapists, representing 3,500 members across all states and territories. This study builds on previous workforce studies, the first of which was conducted in 2004. An online questionnaire was circulated to PACFA members covering participants’ demographics, qualifications, employment, sources of client referrals, client groups and presentations, along with the impact of the COVID-19 pandemic. Reflecting previous findings, participants predominantly identified as female, as coming from Australian or English backgrounds, and as being located in or around major cities. Notably, a higher proportion of counsellors and psychotherapists than psychologists and psychiatrists (who also have qualifications as counsellors or psychotherapists) were found in regional and rural Australia. The shortage of mental health services in Australia, especially in remote areas, and the desire for more working hours among over one quarter of registered practitioners, mean this workforce needs to be far better utilised to meet public demand and reduce health inequities for people in regional, rural, and remote Australia. Government recognition of registered counsellors and practitioners through Medicare’s Better Access subsidised sessions would significantly remedy the shortage of mental health services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".