‘I’m searching for solutions’: why are obese individuals turning to the Internet for help and support with ‘being fat’?
Bibliographic record
Abstract
INTRODUCTION: This study explores what types of information obese individuals search for on the Internet, their motivations for seeking information and how they apply it in their daily lives. METHOD: In-depth telephone interviews with an Australian community sample of 142 individuals with a BMI ≥ 30 were conducted. Theoretical, purposive and strategic samplings were employed. Data were analysed using a constant comparative method. RESULTS: Of the 142 individuals who participated in the study, 111 (78%) searched for information about weight loss or obesity. Of these, about three quarters searched for weight loss solutions. The higher the individual's weight, the more they appeared to search for weight loss solutions. Participants also searched for information about health risks associated with obesity (n = 28), how to prevent poor health outcomes (n = 30) and for peer support forums with other obese individuals (n = 25). Whilst participants visited a range of websites, including government-sponsored sites, community groups and weight loss companies, they overwhelmingly acted upon the advice given on commercial diet websites. However, safe, non-judgemental spaces such as the Fatosphere (online fat acceptance community) provided much needed solidarity and support. CONCLUSIONS: The Internet provides a convenient source of support and information for obese individuals. However, many turn to the same unsuccessful solutions online (e.g. fad dieting) they turn to in the community. Government and community organisations could draw upon some lessons learned in other consumer-driven online spaces (e.g. the Fatosphere) to provide supportive environments for obese individuals that resonate with their health and social experiences, and address their needs.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".