Understanding men’s psychological reactions and experience following a cardiac event: a qualitative study from the MindTheHeart project
Bibliographic record
Abstract
OBJECTIVES: Emotional issues such as depression, anxiety and post-traumatic stress disorder are common following a cardiac event. Despite their high prevalence, they often go undiagnosed and research suggests that men in particular are at higher risk. Therefore, a better understanding of men's experiences with a cardiac event and ensuing health services is key for adapting approaches that meet their needs. The aim of this study was to describe the self-reported emotional challenges that men face following a cardiac event and to understand their patterns of psychosocial adjustment. DESIGN: Qualitative study (focus groups and one-on-one interviews) using an interpretive phenomenal analysis. SETTING: Clinical settings (cardiac departments in hospitals, cardiac rehabilitation programme and family medicine clinics) and in the community in three Canadian provinces. PARTICIPANTS: A total of 93 men participated in the study through 22 focus groups and 5 semi-structured interviews, none has been excluded based on comorbidities. RESULTS: Four major themes emerged: (1) managing uncertainty and adversity; (2) distancing, normalising and accepting; (3) conformity to traditional masculine norms and (4) social, literacy and communication challenges. CONCLUSIONS: Healthcare professionals caring for men following a cardiac event must be aware of the psychological and social adjustments that accompany the physical challenges. However, there is a lack of explicit guidelines, tools and clinical training in men-sensitive approaches. Further research is required to better inform clinical practices and healthcare services.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".