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Record W1961076248 · doi:10.12927/whp.2015.24318

The Effect of the Conflict on Syria’s Health System and Human Resources for Health

2015· article· en· W1961076248 on OpenAlexvenueno aff
Aula Abbara, Karl Blanchet, Zaher Sahloul, Fouad Fouad, Adam Coutts, Wasim Maziak

Bibliographic record

VenueWorld health & population · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal healthWork (physics)Health policyArmed conflictEnvironmental healthHealth workerHealth securityEconomic growthLow and middle income countriesPublic healthInternational healthPolitical scienceHuman healthMedicineHealth servicesDevelopment economicsDeveloping countryNursingPopulationEconomicsEngineeringLaw

Abstract

fetched live from OpenAlex

Prior to the conflict, Syria’s health system was comparable with that of other middle-income countries; however, the prolonged conflict has led to significant destruction of the health infrastructure. The lack of security and the direct targeting of health workers and health facilities have led to an exodus of trained staff leaving junior health workers to work beyond their capabilities in increasingly difficult circumstances. This exodus together with the destruction of the health infrastructure has contributed to the increase in communicable and non-communicable diseases and the rising morbidity and mortality of the Syrian population. Strengthening the health system in the current and post-conflict phase requires the retention of the remaining health workers, incentives for health workers who have left to return as well as engagement with the expatriate Syrian and international medical communities.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.060
GPT teacher head0.331
Teacher spread0.271 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations58
Published2015
Admission routes1
Has abstractyes

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