Development of a patient and clinician co-led education program to promote living well with an implantable cardioverter defibrillator: Insights from a pilot project
Bibliographic record
Abstract
Objective: To evaluate a pilot education program designed to improve patients' experience of living well with an implantable cardioverter-defibrillator (ICD). Methods: Patient Partners with previously implanted ICD and clinicians collaboratively performed monthly education sessions for potential and recent ICD recipients. Curriculum development was informed by current evidence of ICD patients' unique educational needs; delivery format transitioned to a virtual platform following the onset of COVID-19. Participants' experience was evaluated using a tailored questionnaire to explore preliminary insights. Results: 126 participants (median age: 62 years; women: 30%) attended 24 sessions. In-person participants (n = 62, 49.2%) reported sessions as helpful (n = 56, 94%) with regards to format and Patient Partner interactions. Virtual participants 64 (50.8%) completed an electronic survey (n = 27, 45%); reporting sufficient information for most topics with the exception of potential psychological effects of ICD implantation. Patient Partners as collaborative session leaders was perceived to be very helpful (n = 22, 82%) or somewhat helpful (n = 5, 18%). Conclusion: This novel educational partnership met the learning needs of patients at the vulnerable time of new cardiac device implantation of both in-person and virtual formats. Innovation: The inclusion of Patient Partners in co-led cardiac education informs novel approach to care that may improve patients' experiences of living well with complex technology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".