What the newspapers say about medication adherence: a content analysis
Bibliographic record
Abstract
BACKGROUND: This study investigates the coverage of adherence to medicine by the UK and US newsprint media. Adherence to medicine is recognised as an important issue facing healthcare professionals and the newsprint media is a key source of health information, however, little is known about newspaper coverage of medication adherence. METHODS: A search of the newspaper database Nexis®UK from 2004-2011 was performed. Content analysis of newspaper articles which referenced medication adherence from the twelve highest circulating UK and US daily newspapers and their Sunday equivalents was carried out. A second researcher coded a 15% sample of newspaper articles to establish the inter-rater reliability of coding. RESULTS: Searches of newspaper coverage of medication adherence in the UK and US yielded 181 relevant articles for each country. There was a large increase in the number of scientific articles on medication adherence in PubMed® over the study period, however, this was not reflected in the frequency of newspaper articles published on medication adherence. UK newspaper articles were significantly more likely to report the benefits of adherence (p = 0.005), whereas US newspaper articles were significantly more likely to report adherence issues in the elderly population (p = 0.004) and adherence associated with diseases of the central nervous system (p = 0.046). The most commonly reported barriers to adherence were patient factors e.g. poor memory, beliefs and age, whereas, the most commonly reported facilitators to adherence were medication factors including simplified regimens, shorter treatment duration and combination tablets. HIV/AIDS was the single most frequently cited disease (reported in 20% of newspaper articles). Poor quality reporting of medication adherence was identified in 62% of newspaper articles. CONCLUSION: Adherence is not well covered in the newspaper media despite a significant presence in the medical literature. The mass media have the potential to help educate and shape the public's knowledge regarding the importance of medication adherence; this potential is not being realised at present.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".