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Record W2055644427 · doi:10.5539/gjhs.v7n3p111

The Challenges and Recommendations of Accessing to Affected Population for Humanitarian Assistance: A Narrative Review

2014· review· en· W2055644427 on OpenAlexvenueno aff
Shandiz Moslehi, Farin Fatemi, Mohammad Mahboubi, Hossein Mozafarsaadati, Shirzad Karami

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsBureaucracyHumanitarian aidNarrativeNegotiationPolitical sciencePopulationPoliticsConstraint (computer-aided design)Public relationsPublic administrationMedicineEnvironmental healthEngineeringLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: Access to affected people pays an important role in United Nation Organization for Coordination and Humanitarian Affairs (OCHA). The aim of this article is to identify the main obstacles of humanitarian access and the humanitarian organization responses to these obstacles and finally suggest some recommendations and strategies. METHODS: In this narrative study the researchers searched in different databases. This study focused on the data from five countries in the following areas: access challenges and constraints to affected population and response strategies selected for operations in the affected countries by humanitarian organizations. RESULTS: Three main issues were studied: security threats, bureaucratic restrictions and indirect constraint, which each of them divided to three subcategories. Finally, nine related subcategories emerged from this analysis. CONCLUSION: Most of these constraints relate to political issues. Changes in policy structures, negotiations and advocacy can be recommended to solve most of the problems in access issues.

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)0.004
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.141
GPT teacher head0.512
Teacher spread0.372 · 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
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

Citations13
Published2014
Admission routes1
Has abstractyes

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