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Record W4282564535 · doi:10.2196/39300

Acceptability and Usefulness of a Web-Based Motivational Interviewing Session to Improve Nutrition and Oral Health Behaviors of Low-Income Children in Connecticut

2022· article· en· W4282564535 on OpenAlexvenueno aff
Jaclyn Lerner, Kate Killion, Valerie B. Duffy

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMotivational interviewingSession (web analytics)Psychological interventionMedicineBehavior changeOral hygieneFamily medicineGerontologyPsychologyNursing

Abstract

fetched live from OpenAlex

Background Obesity and dental decay are linked through poor diet. In the United States, >13% of 2-5–year-old children have obesity and >21% have tooth decay, with the highest rates in Black and Latino children and those from low-income families. Conflicting information, barriers, and lack of access to healthy food and dental care influence the risk of poor diet and insufficient oral hygiene. Of particular interest is whether leveraging technology can deliver tailored and motivational interventions to promote a healthier diet and oral hygiene behaviors in young children of high-need families. Objective This study aimed to determine the acceptability and usefulness of a web-based motivational interview (MI) and goal-setting session to promote healthy feeding and improve oral health in young children and to determine how an initial survey with tailored messages informs the session to improve the efficiency and effectiveness of goal-setting. Methods Low-income caregivers of children aged 2-6 years were recruited through multiple community agencies. The caregivers completed a web-based nutrition and dental health survey that delivered 2-3 tailored messages to motivate or reinforce healthier target behaviors for their children. Caregivers reported their willingness to change the target behavior of the messages and were invited to participate in an MI session via a web-based videoconference application, facilitated by trained dietitians and dietetics students. The facilitators used the messages received by the caregiver and their willingness to change, to inform the session. The facilitators also used principles of MI to provide evidence-based recommendations, address barriers to these recommendations, and determine feasible goals with participants. Results Of 142 caregivers who completed the initial survey, 83 indicated an interest in the MI session and were contacted. A total of 48 MI sessions were completed (41 with female participants and 24 with non-Hispanic White participants). Caregivers were willing to attempt to make 62 out of the 64 target nutrition behavioral improvements from the initial survey. A total of 24 out of 40 caregivers who received a tailored message to improve a nutrition behavior set a goal based on that message during the MI session. The most commonly set nutrition goals involved increasing vegetable consumption (n=25/48), increasing lean protein consumption (n=8), and serving healthier snacks (n=8), which highlighted the target behaviors that the caregivers deemed most relevant. Of those who provided feedback on the MI sessions (n=41), most strongly agreed (scale from strongly agree to strongly disagree) that the MI session was easy and convenient to attend (n=33), and the UConn nutritionist made them feel comfortable to talk about their children’s health (n=38), helped them think about why health changes may be important (n=32), and helped them set a goal for positive changes in their children’s health (n=34). Conclusions Our results indicate the acceptability and usefulness of a web-based MI and goal-setting session, and that an initial survey with tailored messages informed the goal-setting session. Conflicts of Interest None declared.

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)0.001
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 score0.009
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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