Australia’s alcohol and other drug workforce: National survey results 2019-2020
Bibliographic record
Abstract
The 2019-2020 National Alcohol and Other Drug (AOD) Workforce Survey was undertaken to inform national and jurisdictional workforce planning and workforce development initiatives. This was the first national survey of the Australian AOD workforce since 2005. This report presents the preliminary findings from the National AOD Workforce Survey describing broad trends and themes, pending full publication of our data comprising in-depth analysis. \n \nA total of 1506 workers completed the survey. The majority were employed in the non-government sector (57%) and based in metropolitan locations (64%). Women (69%) outnumbered men 2:1, just over one third (35%) were aged 50-64 years, and 6% identified as Aboriginal and/or Torres Strait Islander, double the proportion in the Australian population. A majority (65%) of workers reported AOD lived experience (personal, family, other), of whom two thirds (63%) declared it to their workplace. The AOD workforce included a diverse range of occupations in various work roles. The largest cohort comprised drug and alcohol counsellors (23%). The majority (71%) of workers indicated their main work role was direct client service provision, and around one quarter (24%) were in a management role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".