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156 The portrayal of osteoarthritis in the UK national newspapers: a preliminary systematic content analysis

2019· article· en· W2939635494 on OpenAlexaff
Kristy Inouye, Heidi Lempp, Charlotte Brooks, Elizabeth Crang, Claire Ballinger, Catherine L. Backman, Jo Adams

Bibliographic record

VenueLara D. Veeken · 2019
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsArthritis Research Centre of CanadaResearch CanadaUniversity of British Columbia
Fundersnot available
KeywordsMedicineNewspaperContent analysisOsteoarthritisContent (measure theory)Physical therapyAdvertisingAlternative medicinePathologySocial science

Abstract

fetched live from OpenAlex

Background: The way the media communicate health-related information has the potential to influence attitudes, beliefs, and behaviours in society. The national newspapers are one source with significant readership across the United Kingdom (UK). Previous studies have examined newspaper articles about physical and mental illnesses, and highlight a tendency to present information in a sensationalized or skewed manner. The representation of gout and rheumatoid arthritis have been examined previously, but there are currently no published studies focusing on the portrayal of osteoarthritis in the newspapers. To address this gap in knowledge, the objective of this study was to explore, examine and systematically analyse the portrayal of osteoarthritis in the UK national newspapers. Methods: This study was undertaken as the first author’s 8-week internship project with Versus Arthritis and the University of Southampton. The Nexis UK database of 16 UK national newspapers was searched for articles with ‘osteoarthritis’ in the title and/or lead paragraph published between July 10th, 2017 - July 10th, 2018 either in print or online. Articles about humans that made a statement or claim pertaining to osteoarthritis were included. Duplicates were excluded. Thematic analysis was undertaken, aided by NVivo 12 Pro software. Results: The Nexis search yielded 204 articles, of which 100 met the eligibility criteria and were used in this preliminary analysis. 21% were from tabloid, 72% from middle market, and 7% from broadsheet/quality newspapers. Osteoarthritis was frequently framed as prevalent, incurable, and with a substantial negative impact on the individual and/or on society. ‘Pain’ was the most commonly referenced symptom, and many articles discussed recommendations to help prevent or manage pain (e.g. diet, supplements, exercise, medication). Three key themes were identified: negative language applied to describe the experience of life with osteoarthritis, positive language applied to present research and product developments, and simplified depictions of interventions (i.e. self-management strategies seem easy/accessible). Data that contributed to the first theme were drawn from across all three categories of newspapers, while the latter two themes reflected content more typically found in the middle market and tabloid newspapers. Conclusion: This study examined and described how osteoarthritis has recently been portrayed in UK national newspapers. The limitations and challenges included short immersion time, analysis of text only, and ambiguity of references to ‘arthritis’ in many articles. Further extended data analysis will generate more in-depth understanding as this was a time-limited preliminary study, and requires ongoing collaboration with Patient and Public Involvement contributors to explore how media messages are received. Future work may incorporate analysis of images that accompany the text of an article to provide additional insight into the article’s overall impact. Better understanding media messages could facilitate improvements in respectful reporting, knowledge translation and dissemination of information. Disclosures: K. Inouye: None. H. Lempp: None. C. Brooks: None. E. Crang: None. C. Ballinger: None. C. Backman: None. J. Adams: None.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.305
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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