Fear and Distrust Within the Canadian Welfare System: Experiences of People With Mental Illness
Bibliographic record
Abstract
While experiences of fear and distrust have been documented as a part of recipients’ interactions with disability benefits, there have been few attempts to explore how they are shaped by system features and their impact on employment pursuits. The purpose of this article is to unpack how fear and distrust emerge among people with mental illness who have recently entered the welfare system. Using an interpretative qualitative approach, the authors draw on the findings from 69 in-depth interviews with key stakeholders about their experiences with employment. Stakeholders included recipients, welfare program and policy staff, and service providers in the community. Data were analyzed by exploring similarities and differences across perspectives and contexts. The findings highlight how system features shape and perpetuate fear and distrust through poorly communicating information about the system, a chaotic state of constant change and complexity, a lack of attention to building trusting relationships between caseworkers and recipients, ongoing system errors, and excessive reporting requirements. The impact of the current state of affairs is significantly harmful to recipients, especially those living with mental illness. Our findings also highlight a possible way forward by building trusting relationships and finding ways to improve communication channels.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.046 | 0.028 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".