Characteristics of Clinicians Are Associated With Their Beliefs About ICD Deactivation: Insight From the DECIDE-HF Study
Bibliographic record
Abstract
Background Discussing goals of care with heart failure patients is recommended but is not done systematically, due to factors such as time and personal beliefs. A recent survey showed that one-fifth of clinicians believe that implantable cardioverter defibrillator deactivation (ICDD) is unethical or constitutes physician-assisted suicide. We investigated whether individuals' characteristics are associated with these beliefs. Methods The Decision-Making About Goals of Care for Hospitalize d Pati e nts With H eart F ailure (DECIDE-HF) survey was given to healthcare providers at 9 hospitals to assess their perceived barriers to goals-of-care discussions. The association between respondent characteristics and their beliefs was examined using 2 adjusted logistic regression models. Results We included 760 clinicians (459 nurses, 94 fellows, and 207 cardiologists). The responses varied among professions, with the belief that ICDD is unethical considered to be important barrier by nurses (24%), fellows (10%), and staff (7%); P < 0.001). After adjusting for site, spirituality being more important in life (odds ratio [OR]: 2.21; 95% confidence interval [CI]:1.37-3.56; P = 0.001, compared to less important), region of training (Asia [OR: 5.88; 95% CI: 2.12-16.31; P = 0.001] and Middle East [OR: 5.55; 95% CI:1.57-19.63; P = 0.008] compared to Canada), and years in practice (OR: 1.32; 95% CI: 1.07-1.63; P = 0.01 per decade) influenced beliefs about ICDD being unethical, with similar results for the belief that ICDD represents physician-assisted suicide. Conclusions Sociocultural factors, region of training, and profession influence clinicians' beliefs about ICDD being unethical and representing physician-assisted suicide. These factors and beliefs must be acknowledged when facing the delicate issue of end-of-life discussion.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".