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Record W2898158687 · doi:10.2196/11619

Body Mass Index Screening and Follow-Up: A Cross-Sectional Questionnaire Study of Pennsylvania School Nurses

2018· article· en· W2898158687 on OpenAlexvenueno aff
Erica Francis, Alicia M. Hoke, Jennifer L. Kraschnewski

Bibliographic record

VenueInteractive Journal of Medical Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsBody mass indexOverweightMedicineCross-sectional studyFamily medicineHealth careChildhood obesityObesityNursingGerontologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood overweight and obesity health concerns can affect a student's academic performance, so it is important to identify resources for school nurses that would help to improve self-efficacy, knowledge, and confidence when approaching parents with sensitive weight-related information and influence overall obesity prevention efforts in the school setting. OBJECTIVE: The purpose of this study was to conduct a Pennsylvania (PA) state-wide 29-item survey addressing school nursing barriers and practices, supplementing information already known in this area. Although the survey covered a range of topics, the focus was body mass index (BMI) screening and its related practice within the schools. METHODS: We conducted a state-wide Web-based survey of school nurses in PA to understand current areas of care, find ways to address child health through school BMI screenings and follow up, and identify current educational gaps to assist school nurses with providing whole child care within the realm of weight management. Chi-square test of independence was conducted to determine the relationship between BMI screening follow up and interest in a BMI toolkit. RESULTS: Nurse participants (N=210), with a 42% (210/500) response rate, represented 208 school districts across PA. Participants were asked about their current process for notifying parents of BMI screening results. The majority (116/210, 55.2%) send a letter home in the mail, while others (62/210, 29.5%) send a letter home with students. A small number (8/210, 3.8%) said they did not notify parents altogether, and some (39/210, 18.6%) notify parents electronically. More than one-third (75/210, 35.7%) of nurses reported receiving BMI screening inquiries from parents; however, under half (35/75, 46.7%) of those respondents indicated they follow up with parents whose child screens overweight or obese. Overall, the vast majority (182/210, 86.7%) do not follow up with parents whose child screens overweight or obese. The majority (150/210, 71.4%) of the nurses responded they would benefit from a toolkit with resources to assist with communication with parents and children about BMI screenings. A significant association between respondent follow up and interest in a BMI toolkit was observed (P=.01). CONCLUSIONS: Schools must start recognizing the role school nurses play to monitor and promote children's health. This goal might include involving them in school-based preventive programs, empowering them to lead initiatives that support whole child health and ensuring opportunities for professional development of interest to them. Nonetheless, the first step in facilitating obesity prevention methods within schools is to provide school nurses with meaningful tools that help facilitate conversations with parents, guardians, and caregivers regarding their child's weight status and health through a BMI screening toolkit.

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)0.011
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.157
GPT teacher head0.614
Teacher spread0.457 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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