The role of travel for people with an eating disorder, an optimal leisure experience
Bibliographic record
Abstract
Introduction Project-based leisure would be conducive to enabling people to project themselves into the future and to move away from the eating disorder, which involves a constant preoccupation with food and one’s body. According to this, travel could be a leisure opportunity that invites to challenge oneself by going out of one’s comfort zone. Coping is a real dilemma for people living with eating disorders and the motivations of a tourism experience could refer to motivation to leave one’s current environment. Consequently, travel would be helpful in reducing the individual’s focus on the illness in a different environment. Objectives The aim of this study is to investigate the use of travel to help people living with eating disorders to live in the present moment and to “let go”. Then, to understand what are the components of travel that are essential for an optimal leisure experience. Methods This multiple case study uses mixed data from a sample of five participants with an ED and living in France. They were invited to live a tourism experience in Québec for one week. This data collection was before, during and after the trip, using the Experience Sampling Method with a mobile app pocket and guided interviews to assess sensations and emotions in the ecological context of patients. Results The trip allowed a letting go and developed a greater ability to live in the present moment. It was a significantly positive moment in the lives of the participants. Conclusions Travel associate with digital diary are an innovative approach for ED. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".