“It feels like I’m the dirtiest person in the world.”
Bibliographic record
Abstract
In summer 2015, a Saudi 1000-bed tertiary care hospital experienced a serious outbreak among patients of Middle Eastern Respiratory Syndrome (MERS); during which, some healthcare providers contracted the virus, but none died. The outbreak provoked not only fear and stress; but also professional, emotional, ethical, and social conflicts and tension among healthcare providers and patients alike. Therefore, this study aims to explore what healthcare providers, who survived coronavirus infection, have experienced as a MERS patient and how the infection affected their relationship with their colleagues. Semi-structured, face-to-face interviews were conducted individually with seven survivors (healthcare providers). Each interview lasted up to 90min, and the data were analyzed using the thematic analysis technique. Within the participants' rich and illuminating experiences, four themes were identified: caring for others in the defining moments, perceived prejudice behaviours and stigmatization, lived moments of traumatic fear and despair, and denial and underestimation of the seriousness of the disease at the individual and organizational levels. Survivors still suffered as a result of their traumatic experiences, which might negatively influence their performance. As these survivors are vulnerable, it is their organization's responsibility to provide a system that embraces HCPs during and after disastrous events.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".