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Record W2998869116 · doi:10.1177/0844562119899310

Perception and Beliefs Regarding Cardiovascular Risk Factors and Lifestyle Modifications Among High-Risk College Students

2020· article· en· W2998869116 on OpenAlexvenueno aff
Dieu-My T. Tran, Catherine Dingley, Rogelio Arenas

Bibliographic record

VenueCanadian Journal of Nursing Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
FundersUniversity of Nevada, Las Vegas
KeywordsOverweightRisk perceptionFramingham Risk ScoreQualitative researchGerontologyMedicinePsychologyPopulationPerceptionRisk factorObesityDiseaseClinical psychologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Studying risk factors and corresponding behavior in young adults is important as atherosclerotic plaque begins to form in early adulthood, resulting in cardiovascular disease (CVD) later in life. The purpose of this study was to explore the perceptions and beliefs regarding cardiovascular risk and lifestyle modification among high-risk college students (based on Framingham 30-year risk score). METHODS: Semistructured qualitative interviews were conducted and analyzed using qualitative content analysis. RESULTS: Risk factors included overweight/obesity, alcohol consumption, elevated blood pressure, family history, and smoking. Qualitative interviews revealed six themes: (a) recognizing risk, (b) lifestyle trajectories, (c) factors influencing lifestyles, (d) ideal healthy lifestyle modifications, (e) perceived benefits of healthy lifestyles, and (f) integrating technology and health apps. Participants demonstrated a lack of understanding of how the various factors contributed to CV risk. Influencing factors to a healthy lifestyle were categorized as environmental, relational, financial, work/life/school balance, and internal/intrinsic motivation. CONCLUSIONS: Understanding high-risk college students' beliefs and perceptions regarding CVD risk factors and lifestyle modification is the first step to assessing the problem facilitating early intervention in the young adult population. Clinicians should assess, develop, and implement risk reduction programs that are tailored to individuals who need it the most, those at high risk.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.021
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.352
Teacher spread0.290 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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