Effectiveness of the Extension for Community Healthcare Outcomes (ECHO) Model of continuing tele-education and influence of key learning conditions on the development of competencies in healthcare professionals: Protocol for a mixed methods systematic review
Bibliographic record
Abstract
Abstract BackgroundThe Extension for Community Healthcare Outcomes (ECHO) Model of continuing tele-education is an innovative guided-practice model aiming at amplifying healthcare professionals’ competencies in the management of chronic and complex health conditions. While data on the effectiveness of the ECHO Model is increasingly available in the literature, the influence of key learning conditions on the model effectiveness remains unclear. Therefore, the overarching aim of this systematic review is to identify, appraise and synthesize the available quantitative (QUAN) and qualitative (QUAL) evidence regarding the effectiveness of the ECHO Model and the influence of key learning conditions on the development of competencies in healthcare professionals.MethodsThis proposed systematic review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology for Mixed Methods Systematic Reviews (MMSR) and will follow a convergent segregated approach. A systematic search will be undertaken using QUAN, QUAL and mixed methods (MM) studies of ECHO-affiliated programs identified in five databases. A publication date filter will be applied to find the articles published from 2003 onwards. Sources of unpublished studies and grey literature will be searched as well. Retrieved citations will be screened by two review authors independently. Disagreements will be resolved through discussion until a consensus is reached, or by including a third reviewer. Studies meeting the predefined inclusion criteria will be assessed on methodological quality and the data will be extracted using standardized data extraction forms. Separate QUAN and QUAL synthesis will be performed, and findings will be integrated using a matrix approach for the purpose of comparison and complementarity.DiscussionThis MMSR will fulfill important gaps in the current literature on the ECHO Model, as the first to provide estimates on its effectiveness and consider simultaneously the influence of key learning conditions on the development of competencies in healthcare professionals. As implementations of the ECHO Model greatly vary depending on the context, topic and targeted professional group, a better understanding of the conditions that contribute to competencies’ development in healthcare professionals is crucial to inform the design and implementation of the model.Systematic review registration:This MMSR protocol is pending registration in the International Prospective Register of Ongoing Systematic Reviews PROSPERO (submitted October 21, 2020; ID Number: 197579).
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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 | 0.143 | 0.148 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.027 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".