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Record W4383652616 · doi:10.1093/eurpub/ckad114

No country is safe from a pandemic: insights into small countries’ COVID-19 experiences

2023· editorial· en· W4383652616 on OpenAlexaff
Sarah Cuschieri, Dritan Bejko, Saverio Stranges

Bibliographic record

VenueEuropean Journal of Public Health · 2023
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusDevelopment economicsEconomic growthGeographyVirologyPolitical scienceMedicineBusinessEconomicsOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The end of 2019—beginning of 2020 imposed unprecedented stress on every country as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) spread across the globe resulting in the Coronavirus 2019 (COVID-19) pandemic. Every country, large and small, fell victim to this burden resulting in governments instituting various mitigation measures to curb the viral spread and protect their population. Small countries, defined as having 2 million or less inhabitants, are regularly overlooked in public health circles and considered featuring similar characteristics as larger countries but at a smaller scale. While this may be true for some aspects, small countries face unique challenges and advantages related to public health governance, healthcare services delivery and economic sustainability. The COVID-19 pandemic is a great example to highlight these factors, while bringing forward lessons learnt that may be translated as crucial evidence for future pandemic preparedness. During the first COVID-19 wave, small countries fared well with low case numbers and fatalities, attributed to the swift implementation of restrictions including lockdowns and closure of borders, especially feasible for island states.1,2 In addition, the reliance on centralized public health systems in most small countries allows for more rapid and coordinated responses, as compared to larger countries often characterized by decentralization and fragmentation of healthcare and surveillance systems.2 Small counties share additional advantageous characteristics that potentially provided the upper hand in the pandemic preparedness including the ease of seeking information transnationally. Public health professionals sit on multiple international round table discussions which provides them with a large network of contacts.3 The geographical size of these countries enables public health systems to adopt swiftly to deal with a crisis including a pandemic. Although small countries face limited public health resources and are highly susceptible to system overwhelm including hospital capacity.2 This results in shifting public health strategies from containment to mitigation,2 with high probability of viral resurgence once restrictions are eased. Tourism plays a crucial role for most small countries, especially those with limited natural resources, for sustainability of their economy, healthcare systems along with other public services.2 Therefore, the closure of borders, airports and seaports (in islands) as part of the COVID-19 restrictions negatively impacted on small countries’ economic sector. This led to an urgency to lift COVID-19 measures as cases subsided, to open the tourism sector. It was anticipated that such actions will induce COVID-19 resurgence. However, those choosing to abruptly lift measures experienced an early increase in case load and mortality burden.1,2 Consequently, instead of luring tourists towards the country, the tourism sector suffered a blow due to the spike in COVID-19 incidence. However, for small countries, whose economy could rely on good budgetary margins, with a strong component of tertiary sector mainly business, financial and communication services it was easier to cope with the pandemic shock and the new working environment imposed by the restrictions. Dependency on neighbour larger countries for goods and workforce is another struggle experienced by small countries especially during COVID-19 lockdown periods. This is vital if an important part of the essential workforce, including hospital and primary care health professionals, are cross border workers. Indeed, one may argue the effectiveness of such measures as landlocked small countries were observed to have experienced similar epidemiological trends as that of their larger country/countries neighbours.1 Although this highlights the importance of cross-country governance and the need for countries not to work in silos. Clear communication between authorities and the public was imperative to ascertain the population follows mandates and later to get inoculated with COVID-19 vaccination. Small countries’ public health authorities have direct contact with central government which limits their public communication independency with the narrative commonly deviated towards the ruling government, irrespective whether it is for the population’s health benefit.4 COVID-19 vaccine access differed across small countries, with those forming part of the European Union procurement agreement having earlier access to approved vaccines.1,5 Procurement of vaccines varied for the rest of the small countries, some undertaking direct agreements with vaccines’ companies, while others relaying on their neighbouring countries.1,5 Although similar vaccination strategies and campaigns were implemented across small European countries, with an overall successful vaccination population coverage even if countries struggled to vaccinate individuals with restricted mobility that could not easily reach the vaccination sites.5 The pandemic challenged every country irrespective of the country size, yet small countries experienced additional hardships pertaining to their ‘smallness’, that is typically overlooked when transnational agendas are formulated. Despite this, it is evident that swift implementation of measures and dissemination of unilateral authorities’ communication to the public are advantages characteristics that could be executed across any country, small or large. Conflicts of interest: None declared.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0090.007
Open science0.0010.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.112
GPT teacher head0.323
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations1
Published2023
Admission routes1
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