Standard Patient History Can Be Augmented Using Ethnographic Foodlife Questions
Bibliographic record
Abstract
The relationship between what and how individuals eat and their overall and long-term health is non-controversial. However, for decades, food and nutrition discussions have often been highly medicalized. Given the significant impact of poor nutrition on health, broader discussions about food should be integrated into routine patient history taking. We advocate for an expansion of the current, standard approach to patient history taking in order to include questions regarding patients' 'foodlife' (total relationship to food) as a screening and baseline assessment tool for referrals. We propose that healthcare providers: (1) routinely engage with patients about their relationship to food, and (2) recognize that such dialogues extend beyond nutrition and lifestyle questions. Mirroring other recent revisions to medical history taking-such as exploring biopsychosocial risks-questions about food relationships and motivators of eating may be essential for optimal patient assessment and referrals. We draw on the novel tools of 'foodlife' ethnography (developed by co-author ethnographer J.J.L., and further refined in collaboration with the co-authors who contributed their clinical experiences as a former primary care physician (D.M.E.), registered dietitian (J.W.M.), and diabetologist (H.Z.)) to model a set of baseline questions for inclusion in routine clinical settings. Importantly, this broader cultural approach seeks to augment and enhance current food intake discussions used by registered dietitian nutritionists, endocrinologists, internists, and medical primary care providers for better baseline assessments and referrals. By bringing the significance of food into the domain of routine medical interviewing practices by a range of health professionals, we theorize that this approach can set a strong foundation of trust between patients and healthcare professionals, underscoring food's vital role in patient-centered care.
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.
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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".