ICD support groups: Are they still relevant now?
Bibliographic record
Abstract
While implantable cardioverter-defibrillators (ICDs) save lives in patients at risk for potentially life-threatening arrhythmias, ICD shocks may prompt fear and anxiety and cause potential psychological trauma in patients. Since the beginning of the ICD's broad use, healthcare professionals recognized the unique demands and experiences associated with this therapy. Some clinics responded by initiating patient-focused meetings broadly referred to as "ICD support groups" to address the mental health challenges in these patients. Decades of research have now underscored the importance of ICD patients' psychological and behavioral processes and their associations with adverse health outcomes. ICD patients frequently report disease-specific mental health symptoms, including ICD shock anxiety, poor device acceptance, body image concerns, and decreased physical activity, that require unique clinical expertise from professionals to effectively manage. The original impetus for ICD support groups persists, but relatively few ICD support groups remain active. Online offerings may have taken on some of these functions. We suggest that ICD support groups remain a strategic intervention to address patient psychological and lifestyle concerns. This paper reviews the value of ICD support groups in the United States, Australia, and Canada and analyzes the current state and potential value of ICD support groups.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".