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Record W2024564737 · doi:10.1080/03630242.2011.556699

Psychological Distress and Self-Care Engagement: Healing After a Cardiac Intervention

2011· article· en· W2024564737 on OpenAlexaff
Maria I. Medved, Niva Piran

Bibliographic record

VenueWomen & Health · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsAttendanceDistressPsychologyContext (archaeology)Self-managementIntervention (counseling)Gene silencingClinical psychologyAnxietyRehabilitationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Silencing the self, a relational concept, occurs when individuals overvalue others' standards, self-sacrifice their needs for others, inhibit self-expression, and experience a sense of dividedness between their inner and outer self. Given the emerging literature highlighting the importance of relational beliefs and experiences in coronary heart disease, the contribution of a concept such as self-silencing to the cardiac healing process is valuable to consider. This study investigated self-silencing dimensions, psychological distress (anxiety and depressive symptoms), and self-care engagement after a serious cardiac event. Forty women and 80 men attending a rehabilitation program completed a series of questionnaires six months post-cardiac intervention. Multivariate regression analyses were performed to examine the role of self-silencing after the influence of cardiac health and sociodemographics were taken into account. Self-silencing was positively associated with anxiety and depressive symptoms for both sexes. For self-care engagement, sex interacted with some of the silencing dimensions. The findings were interpreted in the context of participants' attendance in a rehabilitation program. Women who are self-silencing may benefit from the self-care expectations associated with a cardiac program whereas for men, even engaging in cardiac self-care may be perceived as self-silencing.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.652

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 head0.035
GPT teacher head0.361
Teacher spread0.326 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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