Interprofessional continuing professional development programs can foster lifelong learning in healthcare professionals: experiences from the Project ECHO model
Bibliographic record
Abstract
BACKGROUND: The success of continuing professional development (CPD) programs that foster skills in lifelong learning (LLL) has been well established. However, healthcare professionals often report barriers such as access to CPD and cost which limit uptake. Further research is required to assess how accessible CPD programs, such as those delivered virtually, impact orientation to LLL. Project Extension for Community Healthcare Outcomes (Project ECHO®) is a CPD model that has a growing body of evidence demonstrating improvements in knowledge and skills. Central to this model is the use of a virtual platform, varied teaching approaches, the promotion of multi-directional learning and provider support through a community of practice. This study aimed to explore whether participation in a provincial mental health ECHO program had an effect on interprofessional healthcare providers' orientation to LLL. METHODS: Using a pre-post design, orientation to LLL was measured using the Jefferson Scale of Lifelong Learning. Eligible participants were healthcare professionals enrolled in a cycle of ECHO Ontario Mental Health from 2017 to 2020. Participants were classified as 'high' or 'low' users using median session attendance as a cut-point. RESULTS: The results demonstrate an increase in orientation to LLL following program participation (Pre: 44.64 ± 5.57 vs. Post: 45.94 ± 5.70, t (66) = - 3.023, p < .01, Cohen's d = 0.37), with high ECHO users demonstrating greater orientation to LLL post-ECHO. CONCLUSION: Findings are discussed in the context of self-determination theory and suggest there may be components of CPD programs that more readily support increased motivation for LLL for interprofessional healthcare professionals.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".