Key conditions for the successful uptake and implementation of evidence-based practice in concurrent disorder nursing care with the ECHO Model: insights from a mixed methods study
Bibliographic record
Abstract
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©) 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. This study aimed 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. Methods: We used a mixed-methods convergent design to collect, analyze, and interpret data from nurse participants in the first two years of a Canadian ECHO program for concurrent disorder management. Two studies were conducted simultaneously: (1) an uncontrolled before-and-after study using online surveys at baseline, 6 months, and 12 months to measure changes in nurse-related outcomes, with self-efficacy in concurrent disorder competencies as the primary outcome; and (2) a qualitative, interpretive description study using individual semi-structured interviews with a nurse subgroup, to explore how they developed and implemented competencies, and what factors influenced this process. Quantitative and qualitative results were then merged for comparison and complementarity, using the Pillar Integration Process. Results: Six interrelated conditions were identified for successful uptake and implementation of evidence-based practice in nursing care: (1) Practice and validation opportunities; (2) Reciprocal and trusting relationships in an interprofessional learning environment; (3) Peer-to-peer experience sharing and mentoring; (4) Collaboration with experts; (5) Reinforcement of positive attitudes about professional work in complex or adverse situations; (6) Learning experiences that are team-based, tailored to the setting, and organizationally supported. 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, 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 | 0.148 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| 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".