Bridging the gap: national virtual education programme for professionals caring for adults with intellectual and developmental disabilities at the time of COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic significantly impacted the mental health of adults with intellectual and developmental disabilities (IDD). During this period of uncertainty and need for up-to-date information, various virtual training programmes demonstrated the role of tele-mentoring programmes. AIM: The aim of this paper is to describe the educational evaluation of the National Extension for Community Healthcare Outcomes - Adults with Intellectual and Developmental Disabilities (ECHO-AIDD), a programme for service providers working with adults with IDD during COVID-19. METHOD: The programme consisted of six sessions, conducted weekly, over two cycles. Each session included didactic teaching by hub team members, COVID-19 news updates, wellness check-ins and a brief mindfulness activity, followed by a 30 to 45 min case-based discussion. The hub structure had an inter-professional approach to team expertise. Those with lived experience were an integral part of the content experts' hub. Pre-, post- and follow-up evaluation data were collected. RESULTS: < 0.0001). CONCLUSION: Exposure to National ECHO-AIDD educational intervention led to improvement in perceived competencies. This study also shows the valuable role of people with lived experience in fostering adaptive expertise in learners. The outreach and scalability support the feasibility of building a national virtual community of practice for IDD service providers. Future studies should focus on studying the impact of these programmes on the health outcomes of people with IDD.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".