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Record W3215954457 · doi:10.3389/fpubh.2021.766103

Preparedness and Readiness Strategies for Addressing the COVID-19 Pandemic in Fragile and Conflict Settings: Experiences of the Gaza Strip

2021· review· en· W3215954457 on OpenAlexaff
Samer Abuzerr, Said N. Abu-Aita, Ismail Al-Najjar, A.A. Abuhabib, Heba Al-Jourany, Kate Zinszer

Bibliographic record

VenueFrontiers in Public Health · 2021
Typereview
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPreparednessPandemicPublic healthEconomic growthPopulationSanitationBusinessDevelopment economicsPovertyPolitical scienceEnvironmental healthMedicineEconomicsCoronavirus disease 2019 (COVID-19)DiseaseLaw

Abstract

fetched live from OpenAlex

The COVID-19 pandemic is a global public health threat of serious concern, especially in conflict settings that face fragility and lack adequate resources and capacities. Gaza suffers from a blockade imposed by the Israeli occupation, environmental deterioration, confiscation of lands, demolition of houses and hospitals, restrictions on movement, lack of control over natural resources, and financial constraints. Gaza's population is consequently living in a poor humanitarian situation with high unemployment rates, poverty, over-crowdedness, and a weak health system. This makes Gaza incredibly fragile and affects its ability to respond to the COVID-19 pandemic effectively. The pandemic is expected to deepen Gaza's systems' fragility, which is already overstretched beyond their limits. This will hinder its capacity to deal with the pandemic, and other pre-existing pressing humanitarian needs. Therefore, in this review, we comprehensively explored Gaza's policy failures and successes related to the COVID-19 preparedness and response by state and non-state actors and recommend potential solutions and alternatives. We have addressed critical issues including the health system, water, sanitation, hygiene, socio-economic, education, food security, and others. In Gaza, effectiveness in combating the COVID-19 pandemic can only come from committed political will, transparency from all regulators, strategic dialogue, comprehensive planning, and active international support.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.426
GPT teacher head0.528
Teacher spread0.101 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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