Neoliberalism: Unpacking Limited Employment Success for Persons with SMI
Bibliographic record
Abstract
BACKGROUND.: A mixed-methods case study exploring access to competitive employment for persons with serious mental illness (SMI) revealed limited access to work and low employment success across two northern communities. PURPOSE.: To explore possible explanations for why low employment rates persist despite existing employment services and supports. METHODS.: A total of 46 individual or group interviews were conducted with persons with SMI, vocational providers, and decision-makers regarding access to competitive employment in the case communities. Data were systematically analysed for dominant ideas, interests and institutions using a neo-institutional framework. FINDINGS.: Participants described access to employment to be constrained by provider competition, limited supports, and a lack of consideration of difference-ideas and interests associated with neoliberal influences within provincial employment supports policy. IMPLICATIONS.: Enabling participation in meaningful employment for people with SMI will require occupational therapists to appreciate and contest the oppressive nature of neoliberal policies on local programs and services.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".