Experiences and perceptions of nurses participating in an interprofessional, videoconference-based educational programme on concurrent mental health and substance use disorders: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Individuals with co-occurring mental health and substance use disorders (i.e., concurrent disorders) have complex healthcare needs, which can be challenging for nurses to manage. Providing optimal care for this subpopulation requires nurses to develop high-level competencies despite limited resources at their disposal and the isolated settings in which many of them work. The Extension for Healthcare Community Outcomes (ECHO®) is a promising collaborative learning and capacity building model that uses videoconference technology to support and train healthcare professionals in the management of complex and chronic health conditions. The aim of this study was to explore the experiences and perceptions of nurses participating in a Canadian ECHO programme on concurrent disorders about the competencies they developed and used in their clinical practice, and which factors have influenced this process. METHODS: The study was qualitative, guided by an interpretive description approach. Individual semi-structured interviews were held with ten nurses who had participated in the programme between 2018 and 2020. A thematic analysis was conducted iteratively using an inductive approach to progressive data coding and organization. RESULTS: Four themes and eighteen sub-themes were identified. During their participation in ECHO, the nurses perceived as having further developed eight clinical nursing competencies. Nurses viewed ECHO as a unique opportunity to open themselves to their peers' experiences and reflect on their own knowledge. Learning from experts in the field of concurrent disorders helped them to build their confidence in managing complex clinical situations. The nurses' sense of belonging to a community further enhanced their engagement in the programme, and learning was facilitated through the programme's interprofessional environment. Nevertheless, the lack of contextualized educative content linked to local realities, the limited resources in concurrent disorders, and time constraints were experienced as factors limiting competency development. CONCLUSIONS: ECHO is a promising alternative to conventional, in-person continuing education programmes to improve the development of advanced competencies among nurses providing care to individuals with chronic and complex health conditions. These findings can inform clinicians, educators, researchers, and decision makers who are developing, implementing, evaluating, and escalating future educational interventions in the field of CDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".