Why Don't You Like That? Examining the Relationship Between Adult Eating Habits, Sensory Preferences, Thinking Patterns, and Age
Bibliographic record
Abstract
Picky eating is a common behaviour among adults, yet the existing literature focuses primarily on children. Thus, little is known about the mechanisms behind picky eating in an adult population. This study investigated how sensory sensitivity and intuitive eating are related to picky eating among adults without prior diagnosis of an eating disorder. Participants (N = 142; 81.7% female) were recruited via university communication systems to anonymously complete an online survey. The survey included measures of picky eating (Adult Picky Eating Questionnaire), sensory sensitivity (Glasgow Sensory Questionnaire), intuitive eating (Intuitive Eating Scale 2), stress and anxiety (Depression Anxiety and Stress Scale). Positive correlations were identified between picky eating and sensory sensitivity, anxiety, and stress, but not intuitive eating. Follow-up regression analysis revealed that when sensory sensitivity was included in the model other related factors (stress and anxiety) were unable to account for any additional variance in picky eating behaviour. This key finding suggests that sensory sensitivity is a primary factor in the presence and severity of picky eating behaviours among adults. These findings did not significantly differ between groups based on biological sex, age, ethnicity, or time lived in Canada which suggests that picky eating in adulthood is not restricted to a single population. This study expands our understanding of adult picky eating behaviour and makes suggestions for future research.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".