Evaluation of dedicated COVID-19 hospitals in the pandemic response in Iraq: pandemic preparation within a recovering healthcare infrastructure
Bibliographic record
Abstract
The purpose of this study is to evaluate Iraq's health facility preparedness for the surge of hospitalised cases associated with the ongoing COVID-19 pandemic. In this article, we review pandemic preparedness at both general and tertiary hospitals throughout all districts of Iraq. COVID-19 pandemic preparedness, for the purpose of this review, is defined as: (1) staff to patient ratio, (2) personal protective equipment (PPE) to staff ratio, (3) infection control measures training and compliance and (4) laboratory and surveillance capacity. Despite the designation of facilities as COVID-19 referral hospitals, we did not find any increased preparedness with regard to staffing and PPE allocation. COVID-19 designated hospital reported an increased mean number of respiratory therapists as well as sufficient intensive care unit staff, but this did not reach significant levels. Non-COVID-19 facilities tended to have higher mean numbers of registered nurses, cleaning staff and laboratory staff, whereas the COVID-19 facilities were allocated additional N-95 masks (554.54 vs 147.76), gowns (226.72 vs 104.14) and boot coverings (170.48 vs 86.8) per 10 staff, but none of these differences were statistically significant. Though COVID-19 facilities were able to make increased requisitions for PPE supplies, all facility types reported unfulfilled requisitions, which is more likely a reflection of global storage rather than Iraq's preparedness for the pandemic. Incorporating future pandemic preparedness into health system strengthening efforts across facilities, including supplies, staffing and training acquisition, retention and training, are critical to Iraq's future success in mitigating the ongoing impact of the ongoing COVID-19 pandemic.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".