How Did an Integrated Health and Social Services Center in the Quebec Province Respond to the COVID-19 Pandemic? A Qualitative Case Study
Bibliographic record
Abstract
During the first and second waves of the pandemic, Quebec was among the Canadian provinces with the highest COVID-19 mortality rates. Facing particularly large COVID-19 outbreaks in its facilities, an integrated health and social services center in the province of Quebec (Canada), developed resilience strategies. To explore these diverse responses to the crisis, we conducted a case study analysis of a Quebec integrated health and social services center, building on a conceptualization of resilience strategies using "configurations" of effects, strategies, and impacts. Qualitative data from 14 indepth interviews conducted in the summer and fall of 2020 with managers and frontline practitioners were analyzed through the lens of situations of "anticipation," "reaction," or "inaction." The findings were discussed in three results dissemination workshops, two with practitioners and one with managers, to discern lessons they learned. Three major configurations emerged: 1) reorganization of services and spaces to accommodate more COVID-19 patients; 2) management of contamination risks for patients and professionals; and 3) management of personal protective equipment (PPE), supplies, and medications. Within these configurations, the responses to the crisis were strongly shaped by the 2015 health care system reforms in Quebec and were constrained by organizational challenges that included a centralized model of governance, a history of substantial budget cuts to longterm care facilities, and a systematic lack of human resources.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".