UNION EFFECTIVENESS AND THE COVID-19 PANDEMIC: A CASE STUDY OF ONTARIO LONG-TERM CARE UNIONS
Bibliographic record
Abstract
The COVID-19 crisis in Ontario’s long-term care (LTC) sector has brought unprecedented public attention to long-established systematic weaknesses in funding, staffing, and working conditions that have rendered both workers and residents highly vulnerable to infection. This study seeks to understand why unions have been unable to better protect long- term care workers from vulnerability to COVID-19 by exploring the effectiveness and limitations of unionization and assessing the challenges that unions have faced in safeguarding workers. Eight union representatives amongst SEIU, CUPE, and OPSEU were selected as participants for hour-long semi-structured interviews. Interviews were thematically analyzed for challenges to union power as well as workplace attributes related to COVID protection. Twelve collective agreements were examined to assess the relative strength and weakness of clauses relating to health and safety, paid sick leave, disability benefits, wages, and job security in relation to part-time PSWs. Collective agreements offered limited and varying degrees of protection to workers as unions faced constraints in bargaining within a largely privatized sector under the arbitration- based Hospital Labour Disputes Arbitration Act. The ubiquity of precarious, part-time PSW positions was identified as a major risk factor of COVID vulnerability. Unions also faced four challenges to their effectiveness: the structure of bargaining; challenges in member engagement; the neglect of long-term care and privatization of health-care; and labour relations with the Ford government. In addition to legislative reform concerning staffing and funding, this study suggests that unions engage in deeper forms of worker organizing to develop and exercise labour power beyond the legal confines of the strike-prohibiting HLDAA, as job action elsewhere by feminized healthcare workers has been met with public support and contributed to changes in conditions of care and work.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".