Adapting Hospital Work During COVID-19 in Quebec (Canada)
Bibliographic record
Abstract
Among hospital responses to the COVID19 pandemic worldwide, service reorganization and staff reassignment have been some of the most prominent ways of adapting hospital work to the expected influx of patients. In this article, we examine work reorganization induced by the pandemic by identifying the operational strategies implemented by two hospitals and their staff to contend with the crisis and then analyzing the implications of those strategies. We base our description and analysis on two hospital case studies in Quebec. We used a multiple case study approach, wherein each hospital is considered a unique case. In both cases, work adaptation through staff reassignment was one of the critical measures undertaken to ensure absorption of the influx of patients into the hospitals. Our results showed that this general strategy was designed and applied differently in the two cases. More specifically, the reassignment strategies revealed numerous healthcare resource disparities not only between health territories, but also between different types of facilities within those territories. Comparing the two hospitals' adaptation strategies showed that past reforms in Quebec determined what these reorganizations could achieve, as well as how they would affect workers and the meaning they gave to their 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".