Key conditions for the successful implementation of evidence-based practice in concurrent disorder nursing care with the ECHO® model: Insights from a mixed-methods study
Bibliographic record
Abstract
Background: People with concurrent mental health and substance use disorders have complex biopsychosocial problems but risk not having their healthcare needs met. Nurses are positioned to meet these needs but often lack training in concurrent disorder management. Extension for Community Healthcare Outcomes (ECHO®, University of New Mexico Health Sciences Center, 2003) is a promising technology-enabled collaborative learning model used to implement evidence-based practice and build capacity among healthcare professionals in managing complex, chronic, health conditions. Objective: To understand how an ECHO program for concurrent disorder management impacts nurses' competency development and clinical practice and uncover key conditions for successful uptake and implementation. Design: A convergent mixed-methods design comprising a quantitative, uncontrolled before-and-after study and a qualitative study using interpretive description methodology. Setting and participants: An ECHO program for concurrent disorder management was implemented in 2018 at a quaternary academic hospital centre in metropolitan Western Canada. All 65 nurses who registered in the program between 2018 and 2020 were invited to participate in the study. Methods: = 10) to explore how they developed and implemented competencies and what factors influenced this process. Interview transcripts were analyzed using inductive thematic analysis. Using the Pillar Integration Process, we analyzed results from both methods to provide a richer understanding of the phenomena. Results: We identified six interrelated key conditions for successful uptake and implementation of evidence-based practice in concurrent disorder nursing care with ECHO: (1) Practice and validation opportunities; (2) Reciprocal and trusting relationships in an interprofessional education context; (3) Peer-to-peer experience sharing; (4) Collaboration with experts; (5) Reinforcement of positive attitudes towards one's professional role; and (6) Organizational support. Conclusions: Outcome measures, perspectives, and experiences collected over 12 months indicated that ECHO contributed to nurses' competency development and, under some conditions, to effective nursing practice changes. Given the challenges in implementing clinical guidelines in concurrent disorder nursing care, our results highlight the importance of understanding the key conditions for successful uptake and implementation. This informs approaches to optimally adapt implementation strategies to the needs and specificities of nurses to obtain impactful and sustainable results.
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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 | 0.140 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".