Program esilence 1.0 - self-regulation program in food education via instagram-loricorps, study protocol
Bibliographic record
Abstract
Introduction Social medias are seen as a risk factor for mental health because they increase body dissatisfaction and decrease self-esteem. This program is based on alimentation and physical well-being by relying on integrated intuitive eating and physical self-esteem. This program, implemented in a community setting use social media (i.e. Instagram-Loricorps), is composed of 12 monthly 180-second video capsule that address themes related to the promotion of body sensations and intuitive movement. Objectives The main objective of this study is to evaluate the effects of the program into the physical environment targeting the physical self-perceptions (PSP). Specifically, this study evaluates whether the eSILENCE 1.0 Program improves the level of PSP related to nutrition and explores the changes in the level and variability of the PSP. Methods This project is a mixed sequential explanatory study. 300 participants (Experimental Group [EG; N=200], Control Group [CG; N=100]) are targeted. Online nomothetic questionnaires evaluate occupational changes and PSP in relation to alimentation and are completed by the EG and the CG at pre-test, mid-test and post-test. Online idiographic questionnaires assess PSP and are completed by the EG before and after each video capsule and by the CG once a month without viewing the capsules. Following a preliminary analysis, a focus group will be formed to explain and deepen these results. Participants (N=5) will be recruited voluntarily into the EG. Results to come. Conclusions Analysis of quantitative data will be used to assess the effectiveness of the program and analysis of qualitative data will provide an in-depth understanding of the linkages between the variables. Disclosure No significant relationships.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".