Responses to Hospital Restrictions on Family Visits during the COVID-19 Epidemic in Mali and France
Bibliographic record
Abstract
Few studies have focused on the presence of families in the hospital in the context of an epidemic. The present study aims to contribute to filling this gap by answering the following question: How did professionals, patients and their families cope with more or less drastic restrictions to family visits and presence during the COVID-19 pandemic in a French and a Malian hospital during the COVID-19 pandemic? Data were collected during the first two waves of the pandemic through 111 semi-structured interviews (France = 55, Mali = 56). Most of the interviews were conducted with staff (n = 103), but also with families in the case of Mali (n = 8). The investigators also conducted 150 days of field observations, 44 in France and 106 in Mali. Thematic analysis was applied using an inductive approach. Interviews were content analyzed to identify passages in the interviews that were relevant to these different themes. The study highlighted the difficulty for the medical-clinical system to provide appropriate responses to the many emotional needs of patients in a pandemic context. Families in France benefited from a support service to reduce stress, while in Mali, no initiative was taken in this sense. In both countries, families often used the telephone as an alternative means of communicating with relatives. The results showed that in the two contexts, the presence and involvement of the families contributed to a better response to the patients’ psycho-affective demands and thus promoted resilience in this field.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".