Qualitative content analysis of online news media coverage of weight loss surgery and related reader comments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it