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Record W3185986731 · doi:10.1037/fam0000898

Dyadic patterns of mental health and quality of life change in partners and patients during three months of cardiac rehabilitation.

2021· article· en· W3185986731 on OpenAlexaff
Karen Bouchard, Alexandre Gareau, Katya McKee, Kathleen Lalande, Paul S. Greenman, Heather Tulloch

Bibliographic record

VenueJournal of Family Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversité du Québec en OutaouaisUniversity of Ottawa
Fundersnot available
KeywordsMental healthAnxietyPsychologyRehabilitationPsycINFOQuality of life (healthcare)Depression (economics)Clinical psychologyTransactional leadershipPartner effectsPsychiatryMEDLINEPsychotherapist

Abstract

fetched live from OpenAlex

= 184 dyads) completed questionnaires measuring anxiety, depression, and QoL at baseline (enrollment) and 3 months (discharge). Dyadic data were analyzed using the Actor-Partner Interdependence Model with integrated latent change scores. The results indicated that improved anxiety was associated with significant positive changes in physical and emotional QoL for both the patient and partner (actor effects). A reduction in depression in both partners from baseline to follow-up predicted an increase in emotional QoL for patients and partners, and an increase in physical QoL for partners (actor effects). Patients whose depression decreased from enrollment to the completion of cardiac rehabilitation were associated with partners' greater positive changes in emotional QoL than were patients whose depression did not decrease, and reductions in partners' anxiety over time predicted positive changes in patients' physical QoL (partner effects). Findings underscore the need to screen for and attend to patients' and partners' mental health outcomes postcardiac event, as positive changes in mental health symptoms may optimize changes in patients' and partners' emotional and physical QoL. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.084
GPT teacher head0.440
Teacher spread0.357 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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