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Record W3024895284 · doi:10.1037/rep0000331

Partners’ attachment styles and overprotective support as predictors of patient outcomes in cardiac rehabilitation.

2020· article· en· W3024895284 on OpenAlexfundno aff
Shea E. O'Bertos, Diane Holmberg, Christopher Shields, Lauren Matheson

Bibliographic record

VenueRehabilitation Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNova Scotia Health Research FoundationAcadia University
KeywordsPsycINFOAttendanceRehabilitationPsychologyMediationContext (archaeology)AnxietyAttachment theoryClinical psychologyDevelopmental psychologyMEDLINEPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigates whether the adult attachment styles of support partners in a cardiac rehabilitation context predict their use of overprotective support strategies, and whether such overprotection in turn predicts lower self-efficacy and poorer program attendance in cardiac rehabilitation patients. RESEARCH METHOD: = 35 years). During the first week of a 10-week cardiac rehabilitation program in a midsized rural hospital, participants completed self-report questionnaires that were used to assess partners' attachment styles and levels of overprotection, as well as patients' health-related self-efficacy. Attendance at each session of the program was then tracked by cardiac rehabilitation staff members. RESULTS: A moderated mediation model using bootstrapping showed that when partners were insecurely attached (high in both attachment avoidance and attachment anxiety), a mediational model held, such that more insecure partner attachment predicted more extensive use of overprotective support strategies, which in turn predicted lower patient self-efficacy for exercise and less-frequent program attendance. IMPLICATIONS: Implications for training support partners in more-effective support strategies are discussed. (PsycInfo Database Record (c) 2020 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)0.000
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 score0.044
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.397
Teacher spread0.382 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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