A qualitative exploration of exercise blog believability among emerging adult women
Bibliographic record
Abstract
Emerging adults aged 18–30 years are a unique population who have lifelong experience with digital media. This has contributed to comfort and familiarity with using the internet as a preferred source of information in particular with regard to exercise. Previous research suggests emerging adults may be conflicted about whether or not to believe the information encountered online. The purpose of this study was to explore what aspects of an exercise blog message are believed, and which aspects contribute to personal relevance for a population of emerging adult women. Using a qualitative descriptive design, one-on-one interviews were completed by the principal investigator with ten women (Mage = 25.6 years) residing in a Western Canadian province, all of whom had or were currently attending university. All interviews were recorded and transcribed verbatim. Qualitative content analysis was used to examine and categorise the data, in line with qualitative description designs. Following data analysis, three themes emerged related to blog article believability: information relevance, selective believability, and projecting believability. Exercise blogs may provide an opportunity for some individuals to learn about and diversify personal exercise opportunities. Additionally, participants expressed concern for others, and the potential of these media to contribute to negative outcomes for individuals without prior knowledge in the health disciplines.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".