Quantifying the impact of equity, diversity, and inclusion in electrophysiology: Training the first female electrophysiologists from Jamaica and Saint Lucia
Bibliographic record
Abstract
Background: Delivery of electrophysiology (EP) care in developing nations and underserviced populations faces many hurdles, including the lack of local expertise and knowledge creation. The West Indies has experienced a paucity of local EP expertise. The University of Toronto has undertaken a unique collaborative educational effort with the University of the West Indies. Objective: We describe the effects of equity, diversity, and inclusion (EDI) in EP training at Toronto General Hospital in Canada by quantifying the impact of training the first female electrophysiologists to practice in Jamaica and Saint Lucia. Methods: Data from the ministries of health in Jamaica and Saint Lucia were reviewed. The number of arrhythmia clinic patients seen, EP studies and ablations performed, pacemaker clinic patients seen, and implantable devices, permanent pacemakers (PPMs), and implantable cardioverter-defibrillators (ICDs) implanted were assessed. Results: One hundred one arrhythmia consults were seen by the new electrophysiologist in Jamaica after her return from training in 2020. She has since performed 19 EP studies/catheter ablations at a newly established ablation laboratory. Three cases of left ventricular (LV) dysfunction due to tachy-cardiomyopathy were treated successfully with catheter ablation with immense improvement in LV ejection fraction. Thirteen PPMs, 1 ICD, and 3 LV leads were implanted, after which no early complications were identified. In Saint Lucia, where there is no dedicated electrophysiology laboratory, 2 patients who required catheter ablation for tachycardia-mediated LV dysfunction were identified by the electrophysiologist since her return to the island in 2018. The patients were appropriately referred, resulting in restoration of normal LV function. Six PPMs also were implanted in Saint Lucia. Knowledge translation has been limited by the lack of accessibility to the required devices, catheters, and specialized equipment and accessories, mainly because of their costs. Conclusion: Training the first female electrophysiologists from Jamaica and Saint Lucia led to a quantifiable impact on EP care in both of these Caribbean countries. EDI strategies in EP training programs provide much needed benefits to developing nations, but more support is needed to allow new electrophysiologists to fully utilize their EP training to care for underserviced populations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".