Exercise blog believability among emerging adult women: A qualitative description
Bibliographic record
Abstract
Emerging adults, aged 18 -30 years, make up the largest demographic of internet users worldwide; this population has a lifelong familiarity with digital technology. Social media is a common source of exercise information and is a preferred source of exercise content among emerging adult women. Believability is a user perception that focuses on how the content of a message is perceived by the consumer. Exercise message believability has been correlated with exercise-related attitudes and intentions though it is not known what aspects of the exercise message may influence a consumer's thoughts. Using a qualitative descriptive design to explore this understudied phenomenon, ten emerging adult women, each of whom had at least some university education, and were residing in a Western Canadian province, were asked to read a post from a popular exercise blog. Qualitative content analysis was used to categorize the data collected from one-on-one interviews. The believability of reading the exercise blog post was represented by three themes: information relevance, selective believability, and projecting believability. The women expressed perceptions of believability about familiar aspects of the content regardless of veracity, specifically in cases when the blog's exercise information was related to personal interests or experiences with the exercise or associated outcome. While exercise blogs may provide an opportunity for individuals to learn about new and diverse personal exercise opportunities, participants expressed clear concern for sensationalized and misleading information presented by the blog post, and in particular how this may affect other women.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".