The Australian Psychology Workforce 1: A national profile of psychologists in practice
Bibliographic record
Abstract
Existing workforce data on Australian psychologists are limited and data that exist are problematic. An online survey instrument was developed to profile psychologists including demographics and work characteristics including setting, role, service location and client type. A total of 11,897 completed the survey (response rate 48%) and a subset of these (N = 9,330) who held full registration were included in the current investigation. Participant demographics show a high (75%) proportion of females in the workforce which is particularly evident in the younger age range. Participation in the workforce was high (68%), with main psychology jobs spread relatively equally between the public and private sectors. Over a quarter of participants held a second psychology position, with the majority of second jobs being in private practice. For both first and second jobs the largest proportion spend their time providing counselling and mental health interventions one-to-one to adults. One quarter provide services in non-metropolitan regions, a higher rate than previously reported. Specific population groups such as culturally and linguistically diverse and indigenous clients were prominent in workloads. This study provides a comprehensive profile and provides a rich data source for further exploration of the characteristics of specific groups within the workforce.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".