CALORIE COUNTING APPLICATION FEEDBACK: POTENTIAL IMPACT ON THE TEENAGE FEMALE PSYCHE
Bibliographic record
Abstract
From an early age, girls are surrounded by a desire to be thin. Because of this, eating disorders are a growing epidemic. Technology has been infused into the dietary world, enabling people to diet by themselves as long as Wi-Fi is present. Caloric input applications (apps that count calories) (CCA) have become the efficient way to monitor dietary choices. CCAs use feedback to alert users of proper caloric intake. It was hypothesized that if a diet that ranged between 800-1200 calories a day was entered into a CCA, then feedback generated would be more positive as compared to negative. During three weeks, the dietary choices of the principle investigator (P.I.) were entered into two CCAs. Three dietary profiles were used to simulate the eating habits of an adolescent female. Caloric intake was tracked three times a day and feedback was collected. A certified psychologist classified the feedback. It was determined that there was a relationship between calories entered into the app and the type of feedback generated. Future studies should focus on the development of a CCA that focuses more on when the user is eating rather than calories.Dès l’enfance, les filles sont entourées des messages leur incitant d’un désir d’être minces. En conséquence, les troubles du comportement alimentaire sont devenus une épidémie croissante. La technologie a pénétré le monde diététique, permettant aux gens de suivre un régime eux-mêmes là où le WiFi est présent. Des applications d’apport caloriques (les apps qui calculent des calories) (ACC) sont devenues une façon efficace de controller des choix diététiques. Les ACCs font des observations par rapport à l’information qui a été saisie afin de proposer aux utilisateurs des choix correspondant à une consommation calorique appropriée. L’hypothèse projetée a été le suivant : si un régime comportant entre 800-1200 calories par jour a été saisie dans un ACC, les observations générées par l’app seraient plus positif que négatif. Au cours de trois semaines, l’enquêteur principal (E.P.) a enregistré ses choix diététiques dans deux ACCs. Trois distincts profils diététiques ont été programmés afin de simuler les habitudes alimentaires d’une adolescente. La consommation calorique a été surveillée trois fois par jour et une collecte d’information rassemblée. Un psychologue certifié a analysé la collecte d’information. Il a été conclu qu’il y avait une relation directe entre la saisie des calories dans l’app et les observations correspondent généré par ceci. Des études futures devraient se concentrer sur le développement d’un ACC qui se concentre plus sur l’heure pendant lequel l’utilisateur saisie les données (c’est-à-dire, le moment quand l’utilisateur mange) plutôt que le calcul des calories.
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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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".