Influence of Sex and Gender on Adherence to Self-care Behaviors for Cardiovascular Disease Risk Management in the Global Context
Bibliographic record
Abstract
BACKGROUND: Adherence to self-care behaviors can prevent or delay adverse outcomes associated with cardiovascular disease (CVD). Sex and socioculturally constructed gender might impact individuals' ability to adhere to healthy lifestyles. OBJECTIVE: The aim of this study was to systematically identify, evaluate, and synthesize the literature on the influence of sex and gender on adherence to self-care behaviors for CVD risk management in the global context. METHODS: We searched the MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Global Health Databases for peer-reviewed original articles published between 2013 and 2023. We selected studies that investigated self-care behaviors, self-care maintenance, or self-care management as outcomes and reported sex- and gender-related factors (such as education level, employment status, and marital status). The data were synthesized in a narrative form. RESULTS: The search identified 3540 studies, 52 of which met the inclusion criteria for full-text review. Global North countries accounted for 55% of all the studies. Self-reported questionnaire scores were used in most of the studies (n = 47). Better self-care was associated with being a woman (n = 17), attaining a higher education level (n = 15), and having higher perceived social support (n = 10). The associations between adherence to self-care behaviors and employment status, socioeconomic status, marital status, and household size were inconsistent. CONCLUSIONS: Adherence to self-care behaviors for CVD risk management varied widely, based on gender-related factors. Further research is needed to use a consistent measure of self-care adherence behavior and integrate a wider range of gender-related factors.
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 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".