Adapting Hospital Work During COVID-19 in Quebec (Canada)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it