Positive and negative messages about exercise from The Biggest Loser: Participants thoughts
Bibliographic record
Abstract
People's thoughts about exercise may be influenced by the media through observational learning (Maibach, 2007). This influence may affect behaviour choices. Eighty-five undergraduate students were randomly assigned to one of three video conditions: Positive - The Biggest Loser (BL; n =28); Negative - BL (n =29); Control - reality TV show about singing (n =28). The positive video (PV) showed BL contestants running a marathon and displaying self confidence. The negative video (NV) showed a personal trainer yelling at two contestants to stay on the treadmill. After seeing the clip participants were asked to list five thoughts they had while watching. These statements were coded to understand the participant's thoughts. The codes most cited by participants that watched the NV were: negative description (e.g. brutal) (n=26) and negative trainer (n=27). Overall the trainer in the video was viewed as ÔÇ£meanÔÇØ; however, some participants thought contestants may need this ÔÇ£brutalÔÇØ motivation to lose weight. The code most cited by participants that watched the PV was, negative about marathon (e.g. that is a long marathon) (n=29). Overall participants who watched the positive video thought that a marathon was ÔÇ£really longÔÇØ and they did not think they could do it themselves. These findings are interesting because television shows like BL may be sending mixed messages that influence viewers in ways not yet understood. Further research needs to examine if these messages affect physical activity and weight loss behaviours.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".