Psychological Distress and Self-Care Engagement: Healing After a Cardiac Intervention
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".