Effect of the COVID-19 pandemic on the post-Ebola referral system in Sierra Leone: A descriptive analysis
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic severely impacted health systems in several countries, including Sierra Leone, which was recovering from the 2014-2016 Ebola Virus Disease epidemic. The National Referral Service was established in 2017 followed by launching of the National Emergency Medical Services (NEMS) in 2018 to strengthen the country’s referral system as part of the post-Ebola recovery strategy. This study examined the impact of COVID-19 on the referral system and hospital bed occupancy in Sierra Leone. Methods We conducted a retrospective study using individual-level referral and daily hospital bed occupancy data from 16 public hospitals in Sierra Leone, covering the period October 2017 to February 2022. Data were categorized into four periods: post-Ebola/pre-NEMS (12 months), post-NEMS/pre-COVID (12 months), during COVID-19 (12 months), and post-COVID (12 months). Descriptive statistics was used to analyze the trends in referral and bed occupancy. Results A total of 78,406 referrals were recorded. Referrals increased from 14,870 in the post-Ebola/pre-NEMS period to 27,299 to post-NEMS launch. The number of referrals dropped to 22,948 (during COVID-19) and 13,289 (post-COVID-19). The mean number of referrals per facility initially increased from 1,359 in the post-Ebola and pre-NEMS periods but declined to 1,863 during and after the pandemic. The number of referrals at the provincial level was consistently higher than in Freetown. Bed occupancy rates also declined significantly during the COVID-19 pandemic, with a more pronounced decline in Freetown. Conclusion Our results show a continued decline in patient referrals and bed occupancy even after the peak of the COVID-19 pandemic, with greater impact in the capital Freetown than the provincial regions. The initial increase in referrals and bed occupancy post-Ebola reflects health system strengthening interventions following the Ebola outbreak. However, the COVID-19 pandemic disruptions led to significant declines in both metrics, underscoring the need for sustainable investment in resilient health systems.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".