Factors constraining patient engagement in implantable medical device discussions and decisions: interviews with physicians
Bibliographic record
Abstract
OBJECTIVE: Patient engagement (PE) is warranted when treatment risks and outcomes are uncertain, as is the case for higher risk medical devices. Previous research found that patients were not engaged in discussions or decisions about implantable medical devices. This study explored physician views about engaging patients in such discussions. DESIGN: Qualitative interviews using a basic descriptive approach. SETTING: Canada. PARTICIPANTS: Practicing cardiovascular and orthopaedic physicians. MAIN OUTCOME MEASURES: Level, processes and determinants of PE in medical device discussions and decisions. RESULTS: Views were largely similar among 10 cardiovascular and 12 orthopaedic physicians interviewed. Most said that it was feasible to inform and sometimes involve patients in discussions, but not to partner with them in medical device decision-making. PE was constrained by patient (comfort with PE, technical understanding, physiologic/demographic characteristics, prognosis), physician (device preferences, time), health system (purchasing contracts) and device factors (number of devices on market, comparative advantage). A framework was generated to help physicians engage patients in discussions about medical devices, even when decisions may not be preference sensitive due to multiple constraints on choice. CONCLUSIONS: This study identified that patients are not engaged in discussions or decisions about implantable medical devices. This may be due to multiple constraints. Further research should establish the legitimacy, prevalence and impact of constraining factors, and examine whether and how different levels and forms of PE are needed and feasible.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".