Evidence map›Paper›PMID 40554798›Full record

ArticleJMIR formative research2025

Misrepresentation of Overall and By-Gender Mortality Causes in Film Using Online, Crowd-Sourced Data: Quantitative Analysis.

Calla Glavin Beauregard, Christopher M Danforth, Peter Sheridan Dodds

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Calla Glavin BeauregardComputational Story Lab, University of Vermont, Burlington, VT, United States.ORCID 0009-0001-2424-4769
Christopher M DanforthComputational Story Lab, University of Vermont, Burlington, VT, United States.ORCID 0000-0002-9857-2845
Peter Sheridan DoddsComputational Story Lab, University of Vermont, Burlington, VT, United States.ORCID 0000-0003-1973-8614

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The common phrase "representation matters" asserts that media has a measurable and important impact on civic society's perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society's disease burden. Objective: In this study, for the top 10 major causes of death in the United States, we aimed to examine how cinematic representation overall and by-gender mortality diverges from reality. Methods: Using crowd-sourced data on over 68,000 film deaths from Cinemorgue Wiki, we employ natural language processing techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistics Survey top 10 mortality causes. We parsed, stemmed, and classified each film death database entry, and then categorized film deaths by gender using a specifically trained gender text classifier. Results: Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease (chi-square test, P<.001); 73.6% (477/648) of film deaths from heart disease were men (vs 384,866/695,547, 55.4% in real life) and 69.4% (50/72) of film deaths from cerebrovascular disease were men (vs 70,852/162,890, 43.5% in real life). The 2 exceptions for which women were overrepresented are suicide and accidents (chi-square test, P<.001), with 39.7% (945/2382) deaths from suicide in film being women (vs 9825/48,183, 20.4% in real life) and 38.8% (485/1250) deaths from accidents in film being women (vs 75,333/225,935, 33.5% in real life). Conclusions: We discuss the implications of under- and overrepresenting causes of death overall and by gender, as well as areas of future research.

Indexed as

MortalityMotion PicturesCause of DeathCrowdsourcingFemaleHumansMaleSex FactorsSuicideUnited Statesdata sciencegendermedia representationmortalitynatural language processingNLP

Identifiers

PMID40554798
PMCPMC12212890

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.