CALORIE COUNTING APPLICATION FEEDBACK: POTENTIAL IMPACT ON THE TEENAGE FEMALE PSYCHE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it