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Repertoire of strategies used by French Canadian mothers living in Montreal to pressure their 10‐year‐old children to eat

2005· article· en· W2036025105 on OpenAlexafffundabout
Marie Marquis, Dominique Claveau

Bibliographic record

VenueInternational Journal of Consumer Studies · 2005
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsInformation Technology Association of CanadaUniversité de Montréal
FundersDanone Institute of Canada
KeywordsContext (archaeology)Consumption (sociology)PurchasingMealPsychologyVariety (cybernetics)Food choiceDevelopmental psychologyService (business)MarketingSocial psychologyMedicineBusinessSociologyGeographySocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract This study is concerned with mothers’ use of strategies directed toward their 10‐year‐old children to pressure them to eat. The objective is to document the repertoire of strategies and their context of use. This child‐feeding practice is of interest because of its impact on the child's opportunities for the development of self‐control based on responsiveness to hunger and satiety cues. A total of 24 interviews and one focus group were conducted with French Canadian mothers. Data analyses reveal four major themes associated with the contexts in which pressure to eat were used: food purchasing, food preparation, meal service, food consumption. Strategies related to the context of food purchasing stress the importance of children's influence on family decisions. The strategies used at the time of preparation of meals illustrate the burden of tasks a mother takes on to ensure that her child consumes a particular food. With regard to meal service, very few strategies take into account the appetite of the child. Finally, the variety of strategies deployed at the time of consumption of foods supports the importance of informing parents of the undesirable effect of techniques associated with forcing the child to eat. Avenues for future research are presented.

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 score0.126
Threshold uncertainty score0.969

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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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

Citations3
Published2005
Admission routes3
Has abstractyes

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