Evidence map›Paper›PMID 31198795›Full record

ReviewBulletin of emergency and trauma2019

A Productive Proposed Search Syntax for Health Disaster Preparedness Research.

Behnaz Rastegarfar, Ali Ardalan, Saharnaz Nejat, Abbasali Keshtkar, Mohammad Javad Moradian

Abstract readReview
In one paragraph

Review in Bulletin of emergency and trauma, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Hospitals as disaster victims: Lessons not learned?Journal of the American College of Emergency Physicians open · 2022
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Behnaz RastegarfarDepartment of Disaster Public Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Ali ArdalanDepartment of Disaster Public Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Saharnaz NejatDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Abbasali KeshtkarDepartment of Health Sciences Education Development, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad Javad MoradianDepartment of Disaster Public Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo find a proper search strategy to do a systematic review related to preparedness for disasters.

methodsMeSH and Emtree terms were searched to detect synonyms for two main search terms "disaster" and "preparedness". Expert opinion on the synonyms was examined applying a Google form. The adopted syntax was searched in PubMed and results were sifted. Hand searching in two top key journals was done and sensitivity was calculated.

resultsOut of 1120 articles, 122 were included. In PDM journal, 10 articles were included by hand searching, out of which 5 were not spotted in PubMed search with the proposed syntax. In DMPHP journal, 13 publications were included, with 5 not found in PubMed search. Because of human error in hand searching 2 articles were added.

conclusionThe proposed syntax in this study achieves a sensitivity of search of 0.6 in PubMed which could be quite applicable for researchers. Moreover, in case only MeSH or Emtree terms were applied in search strategy or where hand searching was not performed, there were a number of articles missed.

Indexed as

DisasterHealthPreparedness

Identifiers

PMID31198795
PMCPMC6555207

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.