Comparing Bmi Perception of Self- and Others between Kinesiology and Non-Kinesiology University Students
Bibliographic record
Abstract
The world is experiencing a concerning global increase in the prevalence of obesity and overweight. During the sixties in the United States, the prevalence rate was approximately 10-15% obese and 25-30% overweight ("Overweight," 2017) whereas more recent data displays rates closer to 30% obese and 70% overweight (NHANES, 2014). European countries (Mikolajczyk, Maxwell, El Ansari, Stock, Petkeviciene, & Guillen-Grima, 2010), Indonesia (Hastuti, Rahmawati, & Suriyanto, 2014), Canada, France, Mexico and Switzerland have also reported increased prevalence rates of obesity and overweight ("Obesity Update," 2017). Due to the health consequences associated with weight gain, research studies have examined prevention and treatment options for the increasing rates, but several options have had limited to no success. Maximova, McGrath, Barnett, O'Loughlin, Paradis, & Lambert (2008) and Stice, Shaw, & Marti (2006) hypothesized technological advancements resulting in more sedentary lifestyles, "normalization" of higher weight, and misperception about weight status were potential factors attributing to the programs' lack of successes. Thus, the present study aimed to further examine one of the potential factors: misperception about weight status. OBJECTIVE: The present study examined: 1) how participants perceived their own weight status, 2) how participants perceived others' weight status, and 3) if kinesiology majoring students could better identify BMI classifications than non-kinesiology majoring students. METHODS: A 49-item questionnaire was sent through e-mail to university students attending Illinois State University and Central College. The questionnaire assessed participants perception of their own weight status by self-reporting height (meters) and weight (kilograms) and by selecting a body-size guide (BSG), developed by Harris, Bradlyn, Coffman, Gunel & Cottrell (2008), representing their current body weight status. Perception of others' weight status was assessed by having participants assign a BMI classification to 10 female and 10 male pictorial, body-size guides (BSG). RESULTS: Data is still being collected. However, the results will be presented using general descriptive analysis and group comparisons for perceptions of self and others' weight status. Participants' BMI will be calculated using the following equation: weight (kg) / height (m2).
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".