Bibliographic record
Abstract
We thank Dr. Pippitt, Dr. Junkins, and Ms. Baggaley for their interest in our article on Project ECHO. We are pleased to see discussion being generated from this manuscript. The authors accurately note that our fidelity assessment was limited to quantitative metrics, which limits our ability to extrapolate the ECHO model to chronic disease conditions that do not have clear quantitative outcomes. However, we believe that an assessment of fidelity is important for analyzing the literature to date and to inform future research related to Project ECHO. In Ontario, we are currently using qualitative methodologies to further understand learning and evaluate outcomes for Project ECHO in symptom-based diseases, such as mental health and addictions. While we agree that further exploration of the effectiveness of “learning loops” is needed, we have found in our ECHO Ontario Mental Health program that ECHO is not only beneficial to primary care physicians (PCPs) but can also be beneficial to a broader interprofessional team. There have been previous published studies of ECHO models utilized by other health care professionals, such as pharmacists, social workers, and nurse practitioners, which can expand the learning loops and team engagement. We acknowledge the need for further research to identify attributes of primary care providers engaged in ECHO to inform primary care engagement strategies. We are currently investigating practice attributes of ECHO versus non-ECHO participants and are also using qualitative methodology to better understand the learning process and knowledge transfer mechanisms within an ECHO model focused on mental health. Furthermore, we agree that some ECHO primary care providers may not perceive a need to change practice patterns; however, it is purported that comanagement of cases and iterative reflection during ECHO sessions can be useful in highlighting opportunities for practice improvement. In Ontario, we have encountered similar challenges in the recruitment of PCPs, likely due to the reimbursement model challenges to account for PCP time. Despite these challenges, we have demonstrated high engagement and retention (93%) with other primary care providers, such as nurse practitioners in rural sites. Our review underscores the need for additional evaluation data, using both quantitative and qualitative methods, to determine ECHO’s efficacy and cost-effectiveness in additional symptom-based diseases, such as headache, chronic pain, and mental health. We hope that additional research on ECHO participation and learning will improve Project ECHO implementation efforts in a broad range of contexts and practice settings. Sanjeev Sockalingam, MD, MHPECo-chair, ECHO Ontario Mental Health, and associate professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; [email protected] Carrol Zhou, MDPsychiatry resident, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. Allison Crawford, MD, MACo-chair, ECHO Ontario Mental Health, and assistant professor, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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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 | 0.009 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.038 | 0.062 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".