Online Functional Metacognitive Intervention for Work-Performance Improvement in Adults with Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
Introduction Adult attention deficit hyperactivity disorder (ADHD) is associated with reduced work performance. Online interventions increase accessibility of services to clients by removing barriers such as physical distance, which may prevent care. Objectives This study aimed to assess the efficacy of an innovative functional metacognitive intervention for work-performance improvement of adults with ADHD. Methods This study used a wait-list control group design, with a study and a comparison group (total 46 adults, mean age of 35.65 years). All participants had been diagnosed with ADHD, worked at least 3 months at the same place, and were willing to improve their work performance. Intervention sessions were provided mostly online and focused on the adults’ occupational goals in a workplace context. The intervention’s efficacy was evaluated with a focus on participants’ work performance (Canadian Occupational Performance Measure) executive functions (Behavior Rating Inventory of Executive Function-Adult), organisation in time (Time Organisation and Participation Scale), and quality of life (Adult ADHD Quality of Life Questionnaire). Results Participants’ work performance, executive functions, organisation in time and quality of life significantly improved following the intervention. Their achievements were maintained through to the 3-month follow-up. Conclusions The online metacognitive functional intervention for work-performance improvement of adults with ADHD was found to be efficient and suitable for clinical use among this population. Future studies with larger samples and additional objective measures are needed to further validate these findings. Disclosure No significant relationships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".