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Record W4405425292 · doi:10.2196/59231

Media Framing and Portrayals of Ransomware Impacts on Informatics, Employees, and Patients: Systematic Media Literature Review

2024· review· en· W4405425292 on OpenAlexaboutno aff
Atiya Avery, Elizabeth White Baker, Ishmael Avery, Dream Gomez

Bibliographic record

VenueJournal of Medical Internet Research · 2024
Typereview
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintRansomwareFraming (construction)Internet privacyPosthumanismInformaticsPsychologyMedia studiesComputer scienceWorld Wide WebSociologyEngineeringComputer securityMalwareArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Ransomware attacks on health care provider information systems have the potential to impact patient mortality and morbidity, and event details are relayed publicly through news stories. Despite this, little research exists on how these events are depicted in the media and the subsequent impacts of these events. OBJECTIVE: This study used collaborative qualitative analysis to understand how news media frames and portrays the impacts of ransomware attacks on health informatic systems, employees, and patients. METHODS: We developed and implemented a systematic search protocol across academic news databases, which included (1) the Associated Press Newswires, (2) Newspaper Source, and (3) Access World News (Newsbank), using the search string "(hospital OR healthcare OR clinic OR medical) AND (ransomware OR denial of service OR cybersecurity)." In total, 4 inclusion and 4 exclusion criteria were applied as part of the search protocol. For articles included in the study, we performed an inductive and deductive analysis of the news articles, which included their article characteristics, impact portrayals, media framings, and discussions of the core functions outlined in the National Institute of Standards and Technologies (NIST) Cybersecurity Framework 2.0. RESULTS: The search returned 2195 articles, among which 48 news articles published from 2009 to 2023 were included in the study. First, an analysis of the geographic prevalence showed that the United States (34/48, 71%), followed to a lesser extent by India (4/48, 8%) and Canada (3/48, 6%), featured more prominently in our sample. Second, there were no apparent year-to-year patterns in the occurrence of reported events of ransomware attacks on health care provider information systems. Third, ransomware attacks on health care provider information systems appeared to cascade from a single point of failure. Fourth, media frames regarding "human interest" and "responsibility" were equally representative in the sample. The "response" function of the NIST Cybersecurity Framework 2.0 was noted in 36 of the 48 (75%) articles. Finally, we noted that 17 (14%) of the articles assessed for eligibility were excluded from this study as they promoted a product or service or spoke hypothetically about ransomware events among health care providers. CONCLUSIONS: Organizational response represented a substantial aspect of the news articles in our corpus. To address the perception of health care providers' management of ransomware attacks, they should take measures to influence perceptions of (1) health care service continuity, despite a lack of availability of health informatics; (2) responsibility for the patient experience; and (3) acknowledgment of the strain on health care practitioners and patients through a public declaration of support and gratitude. Furthermore, the media portrayals revealed a prevalence of single points of failure in the health informatics system, thus providing guidance for the implementation of safety protocols that could significantly reduce cascading impacts.

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)0.010
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.382
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.004
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 head0.055
GPT teacher head0.431
Teacher spread0.376 · 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.

Study designSystematic review
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

Citations0
Published2024
Admission routes1
Has abstractyes

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