E-Cog: An online training platform for cognitive health interventions based on the ADDIE model
Bibliographic record
Abstract
Background: Online learning is an accessible and cost-effective solution to deliver training in healthcare. In-person training has long supported mental health practitioners in delivering cognitive health interventions, however for many underserved populations, significant barriers to accessing standardized, evidence-based training for such treatments remain. The E-Cog platform aims to bridge this implementation gap, delivering accessible and engaging remote training that maintains the quality of in-person training. Objectives: Describe the design, development, and implementation of E-Cog, an innovative online training platform for two cognitive health interventions, within the context of a Canadian multisite implementation trial for individuals diagnosed with psychosis. Additionally, examine the feasibility and acceptability of the ADDIE educational framework for e-learning development. Methods: Following the ADDIE model's five phases (analysis, design, development, implementation, and evaluation), we developed our training platform and its curriculum of two cognitive health online certifications: Action-Based Cognitive Remediation and Metacognitive Training. This protocol describes the first four phases of our work and reports on the feasibility and acceptability of the ADDIE model. Conclusions: = 28) was observed during the first two years of implementation. ADDIE was perceived as feasible and acceptable as a framework for e-learning content, depending on flexibility to adapt its structure to research challenges and constraints. Next steps include a qualitative assessment of E-Cog's usability and impact on intervention delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".