Newspaper media framing of obesity during pregnancy in the UK: A review and framework synthesis
Bibliographic record
Abstract
Two thirds of women report experiencing weight stigma during pregnancy. Newspaper media is powerful in framing health issues. This review synthesized UK newspaper media portrayal of maternal obesity. NexisUni was searched to identify newspaper articles, published January 2010 to May 2021, reporting content on obesity during pregnancy. Framework synthesis integrated quantitative and qualitative analysis of the content of articles. There were 442 articles included (59% tabloids and 41% broadsheets). Three overarching themes with interacting sub-themes were as follows: (1) Women were blamed for their weight, risks, and NHS impact. (2) Women were solely responsible for solving obesity, gendered from school age. (3) Women with obesity were a burden on individuals (e.g., themselves, their children, and health professionals), to society, and the NHS. Catastrophizing language framed the "problem," "scale," and "public health concern" of maternal obesity, emphasizing risk, and danger and was alarmist, aggressive, and violent as to elicit fear or devalue women. Articles platformed 'expert' voices rather than women's lived experiences. This review identified that UK newspaper media negatively frames and oversimplifies the topic of maternal obesity. Exposure to blaming and alarmist messaging could increase women's guilt, stigma, and internalized weight bias. The newspaper media should be harnessed to de-stigmatize maternal obesity and promote maternal well-being.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".