The information trail of the ‘Freshman 15’—a systematic review of a health myth within the research and popular literature
Bibliographic record
Abstract
QUESTION: How does health misinformation become part of the American and Canadian vernacular? DATA SOURCES AND SELECTION: Twenty-three databases were searched for articles discussing university freshmen weight gain. Research articles were examined for methodology, number and gender of the participants and weight gain. Popular press articles were reviewed for the types of information published: expert/anecdotal, weight gain, nutrition, exercise, health and alcohol. A timeline of article publication dates was generated. RESULTS: Twenty peer-reviewed, 19 magazine, 146 newspaper, and 141 university newspaper articles were discovered. Appearance of media articles about the 'Freshman 15' mirrored the peer-reviewed articles, yet the information did not reliably depict the research. Research indicated a weight gain of less than five pounds (2.268 kg), while half of the popular press publications claimed a 15-pound (6.804 kg) weight gain. The misinformation was frequently accompanied by information about achieving weight control through diet, exercise, stress reduction and alcohol avoidance. CONCLUSION: Understanding of how the concept of the 'Freshman 15' developed indicates that remediation efforts are needed. Collaborative efforts between health science and academic librarians, faculty and journalists to construct new paradigms for the translation of scientific evidence into information that individuals can use for decisions about health and well-being is suggested.
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 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 | 0.066 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.037 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".