Case Worker’s Perspectives of Ontario’s Social Assistance Program During the COVID-19 Pandemic
Bibliographic record
Abstract
This study explores seven Ontario Works case workers’ perspectives of the social assistance program in Ontario, Canada, during the novel coronavirus pandemic 2019 (COVID-19). Rooted in a critical paradigm, this research was guided by the following research questions: How has the COVID-19 pandemic influenced case worker’s perspectives on the effectiveness of Ontario’s income support program? What gaps do case workers identify in this system and how do they think they would be best addressed? \n \nData for this study was generated through the use of semi-structured interviews and the major findings that emerged include 1) Challenges clients faced while transitioning to virtual service delivery highlight lack of communication and support; 2) COVID-19 emphasizes Ontario Works’ universal service delivery approach and the inability to support people with complex needs; 3) The implementation of CERB highlights Ontario Works’ inflexible program requirements, insufficient assistance rates and who is defined as deserving versus underserving during the pandemic; and 4) Quality of case management service delivery could be improved if case workers experienced less stress, more flexibility and more support from the organization. Participants also identified two major recommendations to address these gaps, including the implementation of a wraparound, wholistic, mixed-methods model that offers financial amounts that meet MBM and inflation, and consult welfare recipients and Ontario Works case worker’s and involve them in the decision-making process.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".