‘Earning and learning’ in those with psychotic disorders: The second Australian national survey of psychosis
Bibliographic record
Abstract
OBJECTIVE: Participation in mainstream education and employment facilitates both the recovery and the social inclusion of people with psychotic disorders. As part of the second Australian survey of psychosis, we assessed labour force activity and participation in formal education among working age adults with psychotic disorders. METHOD: Data were drawn from a large national community prevalence survey of adults with psychotic disorders. Known as the Survey of High Impact Psychosis (SHIP), it was conducted in seven Australian catchment areas during March to December 2010. Current and past year labour force activity, current employment, past year participation in formal education and vocational training, and key clinical and demographic characteristics were examined in a sample of 1825 participants. RESULTS: Only 22.4% of people with psychotic disorders were found to be employed (either full-time or part-time) in the month prior to the survey. In the previous 12 months, 32.7% were employed at some time. Of those in competitive employment, the majority worked part-time (63.9%), while a quarter worked 38 or more hours per week (23.4%). In terms of educational attainment, 18.4% reported difficulties with reading or writing, while 31.9% completed high school, which represents 12 years of formal education. CONCLUSIONS: The proportion currently employed has remained stable at 22% since the last national survey in 1997. Policy makers and service providers could do more to ensure people with psychotic disorders obtain access to more effective forms of assistance with respect to both their continuing education and employment. More effective vocational and educational interventions for people with psychotic disorders appear to be urgently needed.
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) | 0.001 | 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".