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Record W1968869863 · doi:10.1007/s12160-008-9050-y

A Modeling Intervention in Heart Failure

2008· article· en· W1968869863 on OpenAlexaff
Ralph Maddison, Harry Prapavessis, Guy Armstrong, Caleb T.G. Hill

Bibliographic record

VenueAnnals of Behavioral Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWestern University
Fundersnot available
KeywordsSelf-efficacyRandomized controlled trialHeart failurePhysical therapyMedicineCoping (psychology)Health psychologyIntervention (counseling)Internal medicinePsychologyClinical psychologyPublic healthPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Peak oxygen uptake (VO(2)) testing is commonly used to assess chronic heart failure (CHF) patients' exercise tolerance. The test requires maximal effort; however, many participants have low confidence (self-efficacy) to perform optimally. PURPOSE: This randomized controlled trial examined the effectiveness of a modeling intervention to increase Peak VO(2) (PVO(2)) and self-efficacy in people diagnosed with CHF. METHODS: Twenty participants with a diagnosis of CHF were randomized to either an intervention (modeling DVD) or a control group. Both groups completed a measure of self-efficacy prior to performing two PVO(2) tests, each separated by 7 days. After completing the first test (T1) the intervention group watched a 10-min coping model DVD. All participants returned 1 week later (T2) to complete identical study procedures. RESULTS: Analysis of covariance results showed that compared with the participants in the control group, those assigned to the modeling intervention had higher PVO(2) at T2, F (1, 19) = 4.38, p = 0.05, eta (2) = 0.21 and self-efficacy, F (1, 19) = 5.80, p < 0.05, eta (2) = 0.25. Only partial support was found for change in self-efficacy mediating treatment outcome (PVO(2)). CONCLUSIONS: Watching a modeling video is associated with increased PVO(2) and self-efficacy. These results have implications for testing patients in a clinical setting to maximize exercise tolerance test results.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.129
GPT teacher head0.393
Teacher spread0.264 · 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

Citations17
Published2008
Admission routes1
Has abstractyes

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