Evidence map›Paper›PMID 36353313›Full record

ArticleForensic sciences research2022

Exploring technologies to better link physical evidence and digital information for disaster victim identification.

David Lovell, Kellie Vella, Diego Muñoz, Matt McKague, Margot Brereton, Peter Ellis

Open access · goldAbstract read
In one paragraph

Article in Forensic sciences research, 2022. 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
1.7field-weighted citation impact, top 15% of its field
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, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
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

6 authors at 3 institutions in 1 country.

David LovellSchool of Computer Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0002-3938-7586
Kellie VellaSchool of Computer Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0002-1125-5530
Diego MuñozCentre for Design Innovation, Swinburne University of Technology, Melbourne, Australia.ORCID https://orcid.org/0000-0003-2548-1501
Matt McKagueSchool of Computer Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0002-3598-7541
Margot BreretonSchool of Computer Science, Queensland University of Technology, Brisbane, Australia.ORCID https://orcid.org/0000-0002-0982-3404
Peter EllisSchool of Environment and Science, Griffith University, Brisbane, Australia.
Queensland University of Technology · AUGriffith University · AUSwinburne University of Technology · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disaster victim identification (DVI) entails a protracted process of evidence collection and data matching to reconcile physical remains with victim identity. Technology is critical to DVI by enabling the linkage of physical evidence to information. However, labelling physical remains and collecting data at the scene are dominated by low-technology paper-based practices. We ask, how can technology help us tag and track the victims of disaster? Our response to this question has two parts. First, we conducted a human-computer interaction led investigation into the systematic factors impacting DVI tagging and tracking processes. Through interviews with Australian DVI practitioners, we explored how technologies to improve linkage might fit with prevailing work practices and preferences; practical and social considerations; and existing systems and processes. We focused on tagging and tracking activities throughout the DVI process. Using insights from these interviews and relevant literature, we identified four critical themes: protocols and training; stress and stressors; the plurality of information capture and management systems; and practicalities and constraints. Second, these findings were iteratively discussed by the authors, who have combined expertise across electronics, data science, cybersecurity, human-computer interaction and forensic pathology. We applied the themes identified in the first part of the investigation to critically review technologies that could support DVI practitioners by enhancing DVI processes that link physical evidence to information. This resulted in an overview of candidate technologies matched with consideration of their key attributes. This study recognises the importance of considering human factors that can affect technology adoption into existing practices. Consequently, we provide a searchable table (as Supplementary information) that relates technologies to the key considerations and attributes relevant to DVI practice, for readers to apply to their own context. While this research directly contributes to DVI, it also has applications to other domains in which a physical/digital linkage is required, and particularly within high stress environments with little room for error.Key points:Disaster victim identification (DVI) processes require us to link physical evidence and digital information. While technology could improve this linkage, experience shows that technological "solutions" are not always adopted in practice.Our study of the practices, preferences and contexts of Australian DVI practitioners suggests 10 critical considerations for these technologies.We review and evaluate 44 candidate technologies against these considerations and highlight the role of human factors in adoption.

Indexed as

chain of custodydisasterDVIevidenceForensic scienceshuman factorsRFIDtaggingtrackingworkflow

Identifiers

PMID36353313
PMCPMC9639522
OpenAlexW4229001229

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.