Evidence map›Paper›PMID 33166352›Full record

ArticlePloS one2020

Cancer in the news: Bias and quality in media reporting of cancer research.

Amanda Amberg, Darren N Saunders

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

2 authors.

Amanda AmbergSchool of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.
Darren N SaundersSchool of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.ORCID 0000-0003-0924-0513

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer research in the news is often associated with sensationalised and inaccurate reporting, which may give rise to false hopes and expectations. The role of study selection for cancer-related news stories is an important but less commonly acknowledged issue, as the outcomes of primary research are generally less reliable than those of meta-analyses and systematic reviews. Few studies have investigated the quality of research that makes the news and no previous analyses of the proportions of primary and secondary research in the news have been found in the literature. We analysed distribution of study types, research sources, reporting quality, gender bias, and national bias in online news reports by four major news outlets in USA, UK and Australia over six-months. We measured significant variation in reporting quality and observed biases in many aspects of cancer research reporting, including the types of study selected for coverage, the spectrum of cancer types, gender of scientists, and geographical source of research represented. We discuss the implications of these findings for guiding accurate, contextual reporting of cancer research, which is critical in helping the public understand complex science, appreciate the outcomes of publicly-funded research, maintain trust, and assist informed decision-making. The striking gender bias observed may compromise high-quality coverage of research by limiting diversity of opinion, reinforces stereotypes and skews public visibility and recognition towards male scientists. Our findings provide useful guidelines for scientists and journalists alike to consider in providing the most informative and accurate reporting of research.

Indexed as

Biomedical ResearchBiasHumansMass MediaNeoplasmsPeriodicals as Topic

Identifiers

PMID33166352
PMCPMC7652252

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.