Improving the self‐efficacy, knowledge, and attitude of nurses regarding concurrent disorder care: Results from a prospective cohort study of an interprofessional, videoconference‐based programme using the <scp>ECHO</scp> model
Bibliographic record
Abstract
Several challenges have been identified for patients with concurrent disorders to access adequate services and for nurses to care for them. These challenges contribute to a pressing need for continuing educational interventions, particularly within the mental health nursing workforce. To address this issue, an innovative interprofessional videoconferencing programme based on the ECHO® model (Extension for Community Healthcare Outcomes) was implemented in Quebec, Canada to support and build capacity among healthcare professionals for CD management. The aim of this prospective cohort study was to examine nurses' self-efficacy, knowledge, and attitude scores over a 12-month period. All nurses who registered in the programme between 2018 and 2020 were invited to participate in the study (N = 65). The data were collected online using a self-administered survey at baseline, after 6 months, and then 12 months following entry-to-programme. Twenty-eight nurses participated in the study (96.4% women), with a mean age of 39.1 (SD = 6.2). Compared to other professions (n = 146/174), the group of nurses also showed significant improvements in their knowledge and attitude scores, with respective effect sizes of 0.72 and -0.44 at 6 months, and 0.94 and -0.59 at 12 months. However, significant changes in self-efficacy were only found at the 12-month follow-up (P = 0.0213), among the nurses who attended more than 25% of the 20-session curriculum. ECHO is a promising intervention to improve the accessibility of evidence-based practice and to support nurses in suitably managing concurrent disorders. Further research is needed to establish the effectiveness of this educational intervention on clinical nursing practice and patient outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".