Evaluation and Uptake of an Online ADHD Psychoeducation Training for Primary Care Health Care Professionals: Implementation Study
Bibliographic record
Abstract
Background: Health care professionals seldom receive training on neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). An online training was co-developed to address some of the gaps in knowledge and understanding in primary care. A randomized controlled trial demonstrated that the training increased knowledge and confidence and improved practice. Objective: This report highlights the implementation of the training in practice and follow-up 4 years post evaluation. Methods: The online ADHD training comprises 2 modules: "Understanding ADHD" and "The Role of the GP," each taking approximately 45 minutes to complete. The training targets general practitioners primarily but is open to other health care professionals and parents. Feedback was collected through a survey at the end of the training, and the training has been widely adopted by various organizations internationally and nationally. Results: Between December 2019 and January 2024, the "Understanding ADHD" module was accessed more than 13,486 times, while the "Role of the GP" module was accessed 7018 times, primarily by users from the United States and the United Kingdom. Survey results from both modules showed positive feedback with high ratings for usefulness, likelihood to inform practice, and recommendation to colleagues. Some suggestions for improvement included reducing the negative focus on ADHD consequences and incorporating more positive aspects of ADHD. Conclusions: This ADHD online training program, despite facing implementation challenges, has seen positive outcomes, including international translation and high user ratings. Suggestions for improvement were received, but some were not feasible due to regional variations in ADHD pathways. The training's impact extended beyond GPs to other health care professionals, although the COVID-19 pandemic posed obstacles to dissemination efforts. Nonetheless, ongoing plans aim to expand the training's implementation globally.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".