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Record W1965859241 · doi:10.3148/68.2.2007.67

<i>Canadian Dietitians’ Understanding of</i> Non-Dieting Approaches in Weight Management

2007· article· en· W1965859241 on OpenAlexafffundvenueabout
Gail Marchessault, Kevin Thiele, Eleeta Armit, Gwen E. Chapman, Ryna Levy-Milne, Susan I. Barr

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2007
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of British ColumbiaUniversity of Manitoba
FundersCanadian Foundation for Dietetic ResearchHeart and Stroke Foundation of Canada
KeywordsDietingTerminologyWeight managementMedicineContext (archaeology)Portion sizeFocus groupQualitative researchPsychologyMedical educationApplied psychologyWeight lossObesityFood science

Abstract

fetched live from OpenAlex

PURPOSE: How Canadian dietitians define and use non-dieting and size acceptance approaches (SAAs) in the context of weight management was explored. METHODS: Fifteen focus groups with 104 dietitians were conducted in seven Canadian cities. Questions were designed to explore participants' understanding and use of non-dieting and SAAs, including counselling goals, techniques, and outcome measures. Sessions were tape-recorded, transcribed verbatim, coded, and analyzed using qualitative methods. RESULTS: Participants generally agreed that non-dieting involves promoting healthy lifestyles and avoiding restrictive diets. Participants also agreed that size acceptance means accepting all body shapes and sizes and promoting comfort with one's body. Many dietitians said they use size acceptance only with appropriate clients, most often with those who are lighter or without other health risks. Others said that size acceptance, by definition, is appropriate for everyone. Opinions varied about the appropriateness of teaching portion sizes or using meal plans, and whether weight loss could be a goal of non-dieting and SAAs. CONCLUSIONS: Views on the usefulness of non-dieting and size acceptance strategies in weight management counselling were related, at least partially, to the different understanding that dietitians had of these approaches. Terminology needs to be clarified when we speak about non-dieting and SAAs. The varied understanding about these concepts should help dietitians reflect on their own perspectives and practice.

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)0.025
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.293
GPT teacher head0.491
Teacher spread0.198 · 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.

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

Citations12
Published2007
Admission routes4
Has abstractyes

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