Cancer awareness messages in the UK print media: a content analytical and corpus linguistic mixed methods study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».