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Record W2589874118 · doi:10.1097/acm.0000000000001557

In Reply to Pippitt et al

2017· letter· en· W2589874118 on OpenAlexaffabout
Sanjeev Sockalingam, Carrol Zhou, Allison Crawford

Bibliographic record

VenueAcademic Medicine · 2017
Typeletter
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMental Health Research CanadaUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEcho (communications protocol)Mental healthPsychologyMedical educationHealth careFidelityMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.089
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0040.005
Scholarly communication0.0060.010
Open science0.0050.004
Research integrity0.0380.062
Insufficient payload (model declined to judge)0.0120.012

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.

Opus teacher head0.353
GPT teacher head0.536
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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