Qualitative content analysis of online news media coverage of weight loss surgery and related reader comments
Bibliographic record
Abstract
The media has the ability to affect public opinion and policy direction. Prevalence of morbid obesity in Canada is increasing; as is the only effective long-term treatment, weight loss surgery (WLS). Limited research has explored media re/presentations of WLS. The purpose of this study was to examine national online news coverage (and reader comments) of WLS using content analysis. We sought to understand the dominant messages being conveyed within the news texts and reader comments, specifically whose voice was represented, who was the intended audience and what was the overall tone. Articles and comments were retrieved from the Canadian Broadcasting Corporation news web site and analysed using line-by-line techniques. Articles were predominantly 'positive/supportive' (63%) in tone and frequently presented the voices and opinions of 'experts' conveying a biomedical perspective. Comments were overwhelmingly 'negative' (56%) and often derogatory including such language as 'piggy' and 'fatty'. Comments were almost exclusively anonymous (99%) and were frequently directed at other commenters (33%) and 'fat' people (6%). The potentially problematic nature of media framing and reader comments, particularly as they could relate to weight-based stigmatization and discrimination is discussed.
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 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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".