Cancer awareness messages in the UK print media: a content analytical and corpus linguistic mixed methods study
Bibliographic record
Abstract
Background Newspaper readership in the UK is high. Exposure to media stories has been shown to influence reader perceptions and newspapers are frequently used as part of cancer awareness campaigns. However we don’t know what happens to the cancer awareness message when it reaches the print media or whether people featured in cancer-related personal interest stories reflect current cancer inequalities. This study forms the first stage of a PhD and looks at the people featured and the language used to see how cancer is currently reported in the UK print media and how this might influence the public’s awareness and perception of the disease. Methods UK national and regional/local newspaper articles featuring a personal interest story about an individual’s journey with ovarian cancer over a seven-and-a-half year period were identified from the Nexis database. Content analytical methods were used to code information about the newspaper, demographic information about the people featured, and key cancer awareness information such as whether a list of symptoms was provided, or whether early detection was linked to better survival. WMatrix3 was used to conduct corpus linguistic analyses of the language used in the articles including key words, themes, and patterns of words appearing together by comparing the articles to a corpus of standard written English (British National Corpus Written Sampler). Results Newspaper coverage decreased with increasing age; only 34.51% (n=156) of articles featured individuals aged over 50. Managers/professionals were featured twice as often as non-professionals (14.82%, n=67 vs 29.8%, n=68). Only a quarter (26.77%, n=121) of articles provided a list of symptoms and even fewer linked early detection and survival (16.81%, n=76) or described the age group most at risk (13.05%, n=59). Corpus linguistic analyses utilising log likelihood (LL) across the years revealed distinctly negative use of language reflecting sadness (LL=+17.03, n=36 [2006] to LL=+72.13, n=105 [2009]) and death (LL=+16.27, n=92 [2012] to LL=+114.29, n=139 [2007]), as well as frequent use of battle language. Conclusions Stories about an individual’s journey with ovarian cancer in UK newspapers tend to be negative, lack educational content and do not reflect those most at risk. The next steps of the project are: 1) tracking specific campaigns through the print media to see what happens to the message and how any related personal interest stories are presented 2) understanding why articles are presented in this way through interviewing press release officers, journalists and editors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".