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The message writing process behind SmartAPPetite, a smartphone application for improving food knowledge and dietary behaviours among high school adolescents.

2019· preprint· en· W4212874336 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEducational Methods and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)PsychologyComputer science

Abstract

fetched live from OpenAlex

Purpose: Poor dietary behaviours in adolescence can carry into adulthood and contribute to the development of chronic disease; consequently, adolescence is a critical time to establish healthy dietary habits. Since the majority of adolescents own smartphones, smartphone-based interventions to improve food knowledge and dietary behaviours are a logical approach. The objective of this abstract is to describe the message-writing process that was developed to ensure consistent, evidence-based nutrition messages for a smartphone application. Process: SmartAPPetite is a multidimensional application that sends messages to help users make healthier choices. It was developed through an interdisciplinary collaboration with an overall goal of improving food knowledge, food purchasing, and diet quality of adolescents. Systematic approach used: A database of over 1000 messages was created with a range of nutrition and lifestyle topics, such as sports nutrition, eating away from home, information about specific nutrients, seasonality and origin of foods, and how to choose, prepare, and store various fresh food items. A Youth Advisory Council of high-school students assisted with the selection of topics and assessing the relatability of messages. A writing guide was created and used to standardize the messages which included dietitian-approved sources to gather nutrition information. Messages were written by undergraduate and masters level nutrition students, edited by senior writers, and approved by dietitians. Using program algorithms, SmartAPPetite selected messages from the database according to the user's age, sex, and reported dietary preferences. User feedback also allowed the app to continually adjust message selection algorithms. Conclusions: SmartAPPetite messages have undergone a thorough planning, writing, editing, and approval process to ensure users are provided with evidence-based, expert recommended nutrition and lifestyle messages. Recommendations: A systematic approach must be used to ensure nutrition and healthy lifestyle messages are of high-quality and evidence-based. Significance to field of dietetics: Nutrition-related smartphone applications have the potential to reach a large proportion of Canadian adolescents and enhance dietary behaviours.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.362
Teacher spread0.331 · 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

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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