The emergency medical services network’s response to the COVID-19 pandemic in Albania
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, healthcare systems worldwide have implemented many health emergency plans to address the crisis. Following initial predominantly hospital-centred approaches, community-based healthcare assistance emerged as a more effective response to the emerging population needs. In low-middle-income countries, and particular in the so-called transition countries, the adaption the complexities of integrating pre-hospital and in-hospital Emergency Medical Services (EMSs) have been particularly challenging due to the absence of a consolidated network among these services. This research aimed to evaluate the emergency healthcare services response to covid-19 pandemic in Albania, as significant transition country. Method: The country case study methodology was deemed the most fitting approach for this research. Albania was selected as a notable case study due to its continuous endeavours towards achieving national welfare aligned with European standards, especially in the healthcare sector, as it has been moving towards pre-adhesion to the European Union. Results: Albanian EMSs network demonstrated its capability to update over time the national strategical plan against COVID-19 pandemic according to emerging evidence and the related organizational issues to effectively satisfy population health needs. This adaptability became feasible with the introduction of a modern EMSs system, comprising both pre-hospital and in-hospital dimensions. These two components collaborated and are still collaborating to implement integrated healthcare pathways, each with distinct responsibilities, resources, and protocols. Conclusion: The development, consolidation, and collaboration between pre-hospital and in-hospital EMSs implemented in Albania have played a crucial role in preventing the collapse of the healthcare system in the face of the COVID-19 pandemic. Albanian experience provides valuable insights for the reform or to build up EMSs network and healthcare systems in transition countries, drawing upon the lessons learned from the challenges posed by the 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".