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Record W4386583191 · doi:10.5812/semj-133707

Investigating the Level of Preparedness of Iranian Hospitals against CBRN Incidents: A Case Study of Hospitals in West Azerbaijan Province

2023· article· en· W4386583191 on OpenAlexaboutno aff
Saeid Beikmohammadi, Bagher Amirheidari, Tania Dehesh, Mahmood Nekoei‐Moghadam, Vahid Yazdi‐Feyzabadi, Ebrahim Hassani, Hossein Habibzadeh

Bibliographic record

VenueShiraz E-Medical Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistPreparednessRadiological weaponMedicineDescriptive statisticsMedical emergencyCronbach's alphaEnvironmental healthFamily medicinePsychology

Abstract

fetched live from OpenAlex

Background: Hospitals are the front line of dealing with Incidents. Chemical, biological, radiological, and nuclear (CBRN) incidents are alarming for governments' healthcare providers and the public. Therefore, they must make the necessary preparations to deal with these incidents. Objectives: This study aimed to evaluate the preparedness of hospitals against chemical, biological, radiological, and nuclear incidents and the related influential factors. Methods: The present study was a cross-sectional survey in northwest Iran, 2020-2022. The statistical population was the hospitals of West Azerbaijan province. The inclusion criteria were that hospitals must be university or therapeutic affiliated with the West Azerbaijan University of Medical Sciences, and at least one year had to be passed since the hospital’s operation. Also, the exclusion criteria were that the hospitals were on the verge of closing or changing their use. In this way, 26 hospitals in West Azerbaijan were studied. The "Canadian Center for Emergency Preparedness" evaluation checklist research tool was used to determine the level of preparedness of the studied hospitals in CBRN incidents. The data was collected for 5 months, from January to May 2021. Cronbach's alpha score for this checklist was 0.94. Descriptive and analytical statistics indicators were used for data analysis using SPSS 20 software. Results: The study showed that the hospitals lacked the preparation, capacities, and abilities to deal with CBRN incidents. In the single-variable mode, in the chemical dimension, the number of morgues of the deceased (P = 0.006); in the biological aspect, per capita educational factors in the biological domain (P = 0.03), the number of facility personnel (P = 0.04), the number of infectious disease specialists (P = 0.02), the number of equipment with optimal laboratory capabilities (P = 0.04), and the number of morgues of the deceased (P = 0.006); in the radiological and nuclear dimensions per capita of nuclear education (P = 0.01) and dosimeter (P = 0.03), and the general dimension the CBRN training per capita (P = 0.004), the number of personnel (P = 0.015), and laboratory equipment (P = 0.006) had a significant relationship with the preparedness of hospitals against CBRN incidents (P < 0.05). Conclusions: Overall, this study's results showed that hospitals' preparedness against CBRN incidents was unsatisfactory, and appropriate policies needed to be adopted to improve it.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.114
GPT teacher head0.430
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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