Project ECHO Rheumatology – Rationale and Results from a Multi-Method Study to Capture Impact
Bibliographic record
Abstract
Context Project ECHO (Extension for Community Healthcare Outcomes) is a virtually-delivered health professions education model, designed to improve patient care by enhancing primary care capacity in specialty topics. Launched in 2017, Project ECHO Rheumatology (‘ECHO’) has welcomed over 500 primary care clinicians provincially to learn about rheumatic disease diagnoses and management. Qualitative and quantitative data pertaining to provider self-efficacy, satisfaction, knowledge, and practice change have been rigorously collected since its inception. Owing to the protean clinical presentations, heterogeneous diagnoses discussed in each patient case presented, and varied management approaches, capturing impact regarding clinical outcomes has proven challenging. Objectives To evaluate ECHO impact on clinicians by 1) exploring experiences in ECHO and its impact on rheumatic disease management and 2) assessing the impact of ECHO on clinicians’ self-efficacy and knowledge. Methods We adopted a multi-method study design, where qualitative and quantitative components of this study were conducted. Descriptive statistics, paired samples t-tests, and effect sizes were calculated from pre-post questionnaire results. The qualitative descriptive approach was used to analyze focus group discussions. Results Through analysis of both qualitative and quantitative components, ECHO impacted clinicians in multiple ways: clinicians increased in self-efficacy in managing rheumatic conditions (p<.001), perceived increases in knowledge, benefited from ongoing mentorship and a supportive community of practice, and integrated teachings from weekly sessions into their clinical practice. Clinicians from rural and Northern Ontario particularly benefited as access to specialists in their areas was sparse to none. Clinicians also increased in their awareness of interprofessional rheumatic management, utilizing pharmacy, nursing, occupational therapy, and physiotherapy to their full potential. Ultimately, primary care clinicians were able to better manage rheumatic conditions within primary care, using specialists and the larger health care system more wisely. Conclusion The burden of rheumatic disease is rising. ECHO is a promising education model that builds capacity within primary care to manage rheumatic conditions more adeptly and wisely. The multimethod research approach permitted a wholistic analysis and synthesis of rich qualitative and quantitative data.
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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 | 0.238 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".