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Record W2943838628 · doi:10.1097/jcn.0000000000000584

Exploring the Mechanism of Effectiveness of a Psychoeducational Intervention in a Rehabilitation Program (CopenHeartRFA) for Patients Treated With Ablation for Atrial Fibrillation

2019· article· en· W2943838628 on OpenAlexaff
Signe Stelling Risom, Johanne Lind, Victoria Vaughan Dickson, Selina Kikkenborg Berg

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRehabilitationAtrial fibrillationMechanism (biology)AblationIntervention (counseling)MedicineInternal medicineCardiologyPhysical medicine and rehabilitationPhysical therapyPsychiatryPhilosophy

Abstract

fetched live from OpenAlex

BACKGROUND: Patients treated for atrial fibrillation with an ablation can experience decreased mental health. Little is known about the effect of a psychoeducation intervention on this patient group. OBJECTIVES: The aim of this study was to explore the effect of a psychoeducation intervention on patients' mental health after participating in a cardiac rehabilitation program, with a focus on elaborating on the lack of mental health improvements. METHOD: Sequential explanatory mixed methods including secondary analysis of qualitative and quantitative data collected in a randomized rehabilitation trial was performed. Perceived health was measured by a questionnaire (n = 95), and qualitative interviews were performed (n = 10). RESULTS: Patients scoring high on perceived health experienced positive effects of the intervention. Patients scoring low appear to have either low physical capacity and severe atrial fibrillation symptoms, bigger life issues, or lack of social support. CONCLUSION: A more in-depth understanding of the effect of a psychoeducational intervention included in a cardiac rehabilitation program has been achieved.

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)0.003
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 score0.729
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.056
GPT teacher head0.340
Teacher spread0.284 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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