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Record W2903991454 · doi:10.1016/j.ijchp.2018.11.004

The role of vital exhaustion in predicting the recurrence of vascular events: A longitudinal study

2018· article· en· W2903991454 on OpenAlexaff
Piroska Balog, Barna Konkolÿ Thege

Bibliographic record

VenueInternational Journal of Clinical and Health Psychology · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsWaypoint Centre for Mental Health CareUniversity of Toronto
FundersHungarian Scientific Research Fund
KeywordsHostilityPsychologyAnxietyDepression (economics)Psychological interventionBody mass indexClinical psychologyMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVE: The aim of this study was to examine the role of vital exhaustion in predicting the recurrence of vascular events. METHOD: = 14.7 years), 395 (48.4%) of whom reported treatment for the reoccurrence of a vascular event during the four-year follow-up period. Concurrent effects of baseline vital exhaustion (measured by a shortened version of the Maastricht Questionnaire), depression (assessed by a shortened version of the BDI), anxiety (assessed by the HADS), and hostility (assessed by a shortened version of the Cook-Medley Hostility Scale) in predicting the recurrence of T2 vascular events were examined. The analyses were also controlled for traditional risk factors, such as age, education, body mass index, smoking, alcohol use, and lack of physical activity. RESULTS: The regression analyses showed that vital exhaustion scores significantly predicted the reoccurrence of vascular events even after controlling for all covariates. None of the other psychological predictors (depression, anxiety, and hostility) was significant in the final model. CONCLUSIONS: These results suggest that despite the partial conceptual overlap with several similar constructs, vital exhaustion is a distinct phenomenon that deserves consideration when planning and implementing interventions to reduce the risk of vascular diseases.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.095
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.144
GPT teacher head0.578
Teacher spread0.433 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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