Supporting Reassigned Hospital Staff During the COVID-19 Pandemic in the Montreal Region: What Does it say About Leadership Styles?
Bibliographic record
Abstract
Globally, the COVID-19 pandemic took a high toll on health human resources, especially in contexts where these resources were already fragile. In Quebec, to make up for the shortage of health human resources, and to contain the COVID-19 outbreaks in long-term care facilities, many hospital staff (including a majority of nurses) were sent to those facilities, with varying degrees of support. Building on the body of evidence linking leadership style and resilience, we conducted a qualitative comparative analysis of two hospitals in the Montreal Metropolitan Area, Quebec. We explored respondents' experience of psychosocial support tools provided to hospital staff reassigned to COVID-affected facilities. Data from 27 in-depth interviews with high- and mid-level managers, and front-line workers, was analyzed through the lens of leadership styles. Our findings highlighted how the design and implementation of support tools revealed major differences across the two hospitals' leadership styles (i.e., one hospital expressing leader-centered styles vs. the other expressing follower-centered leadership styles). The expression of these leadership styles was largely shaped by recent policies, notably a major political reform of 2015, which enforced more centralized decision-making. Our study offered additional empirical evidence that leadership styles fostering the recovery of health human resources may be a key indicator of successful response to crises.
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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".