Qualitative Analysis of Patient Decisional Needs for Medications to Treat Heart Failure
Bibliographic record
Abstract
BACKGROUND: The development of tools to support shared decision-making should be informed by patients' decisional needs and treatment preferences, which are largely unknown for heart failure (HF) with reduced ejection fraction (HFrEF) pharmacotherapy decisions. We aimed to identify patients' decisional needs when considering HFrEF medication options. METHODS: This was a qualitative study using semi-structured interviews. We recruited patients with HFrEF from 2 Canadian ambulatory HF clinics and clinicians from Canadian HF guideline panels, HF clinics, and Canadian HF Society membership. We identified themes through inductive thematic analysis. RESULTS: Participants included 15 patients and 12 clinicians. Six themes and associated subthemes emerged related to HFrEF pharmacotherapy decision-making: (1) patient decisional needs included lack of awareness of a choice or options, difficult decision timing and stage, information overload, and inadequate motivation, support and resources; (2) patients' decisional conflict varied substantially, driven by unclear trade-offs; (3) treatment attribute preferences-patients focused on both benefits and downsides of treatment, whereas clinicians centered discussion on benefits; (4) quality of life-patients' definition of quality of life depended on pre-HF activity, though most patients demonstrated adaptability in adjusting their daily activities to manage HF; (5) shared decision-making process-clinicians' described a process more akin to informed consent; (6) decision support-multimedia decision aids, virtual appointments, and primary-care comanagement emerged as potential enablers of shared decision-making. CONCLUSIONS: Patients with HFrEF have several decisional needs, which are consistent with those that may respond to decision aids. These findings can inform the development of HFrEF pharmacotherapy decision aids to address these decisional needs and facilitate shared decision-making.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".