Perceptions of Food and Eating Among Chinese Patients With Cancer
Bibliographic record
Abstract
This article explores the ways that participants in a Chinese cancer support group talk about food, diet, and eating. An ethnographic research design was used, including participant observation at a Chinese cancer support group over an 8-month period and key informant interviews with 7 members of the group. Food, eating, and diet were a recurrent focus of discussion at support group meetings throughout the fieldwork period. The ways in which support group participants talked about food centered on 3 distinct but interconnected themes: the prevalence of eating issues as an adverse effect of cancer and its treatment, the importance of eating ability, and questions and concerns connected with the differing and often contradictory cultural models of diet that they were exposed to. Culturally specific understandings of the relationship between food and health informed Chinese patients' experience of eating issues during cancer treatments and their ongoing concern with food and nutrition after the completion of treatment. Health professionals need to pay more attention to the meanings and attributes of food and eating beyond their physiological properties, and further research needs to be conducted with other immigrant populations with culturally distinct understandings of food.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".