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Record W2399049953

Effects of an interdisciplinary education program on hypertension: A pilot study.

2013· article· en· W2399049953 on OpenAlexaff
Thérèse A Lauzière, Nicole Chevarie, Martine Poirier, Anouk Utzschneider, Mathieu Bélanger

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsVitalité Health Network
Fundersnot available
KeywordsBlood pressureAnthropometryMedicinePhysical therapyIntervention (counseling)Quality of life (healthcare)Health educationGerontologyNursingInternal medicinePublic health
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The goal of this pilot study was to examine the effects of a structured interdisciplinary education program on blood pressure, knowledge, anthropometric measures, medication compliance, behavioural risk factors and quality of life. METHOD: In this quasi-experimental study, participants were assigned to an intervention (n = 21) or a regular care group (n = 19). The intervention group attended four weekly sessions related to hypertension. Anthropometric measures and blood pressure were recorded at baseline, one, three and six months for all participants. Both groups completed questionnaires on knowledge, health-related behaviours and quality of life at these same intervals. RESULTS: The reduction in systolic blood pressure was greater in the intervention group than in the regular care group (p = 0.05). However, there were no between group differences with regard to the other variables studied. CONCLUSION: Participation in a structured interdisciplinary education program was associated with a reduction of systolic blood pressure, thus contributing to a risk reduction for cardiovascular disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.420
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations20
Published2013
Admission routes1
Has abstractyes

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