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Record W2026239565 · doi:10.1097/ncc.0b013e3181982d36

Perceptions of Food and Eating Among Chinese Patients With Cancer

2009· article· en· W2026239565 on OpenAlexaff
Kirsten Bell, Joyce Lee, Svetlana Ristovski‐Slijepcevic

Bibliographic record

VenueCancer Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsFocus groupMedicinePerceptionSupport groupFood groupEthnographyPsychologyPsychiatryEnvironmental healthSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.351
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2009
Admission routes1
Has abstractyes

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