La résilience de l’hôpital du Mali face à la COVID-19 dans un contexte de pénuries
Bibliographic record
Abstract
AIM: The objective of this research is to report the strategies of resilience mobilized by the Hospital of Mali to face Coronavirus disease (COVID-19). METHOD: The data collected within the hospital covered the first months of the pandemic (April to July 2020). A total of 32 semi-structured interviews and 53 observation sessions were conducted. Data analyses were based on a conceptual framework and were conducted using a deductive approach. RESULTS: The results show that, due to the multiple effects of the COVID-19 such as the aggravation of staff penuries, the high workloads, the need to create dedicated infrastructures, the drastic decrease in revenue due to the drop in hospital's attendance, the hospital and its staff implemented multiple strategies (e.g., reduction or postponement of some expenses, requisition of facilities, recruitment of contractual staff and redeployment of some healthcare workers). Those strategies generally allowed to maintain patients access to care, although there were many restrictions for non-COVID-19 patients. The hospital was able to build absorptive resilience. CONCLUSION: This qualitative research provides a better understanding of hospitals' resilience processes to the COVID-19 pandemic in a hospital setting. Lessons learned from this study should help hospitals managers to design more appropriate and effective responses to future health crises.
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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 | 0.004 | 0.007 |
| 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 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".