Top ten priorities identified by healthcare professionals to support the clinical care of individuals with attention-deficit/hyperactivity disorder: A Canadian Delphi study
Bibliographic record
Abstract
BACKGROUND: Limited research exists on key issues that healthcare professionals perceive as important for optimizing Attention-Deficit/Hyperactivity Disorder (ADHD) care. This study identified the top ten priorities that healthcare professionals consider vital to support ADHD care in Canada. METHODS: A three-round online Delphi study was conducted using electronic surveys from 2022-2024 across Canada. In Round 1, healthcare professionals were asked to rate 21 predetermined items using a 5-point Likert scale and re-evaluated those rankings in Round 2. In Round 2, a new set of 34 items identified from Round 1 were rated and the rankings re-evaluated in Round 3. Consensus was determined by percentage agreement ≥ 90% with a Likert score ≥ 4. For each priority item, the mean Likert score, standard deviation, 95% confidence interval (CI), and the minimum and maximum Likert scores were calculated. RESULTS: 96 Canadian healthcare professionals completed Round 1. 82 (85% response rate) completed Round 2 and 73 (89% response rate) completed Round 3. The two highest ranked priorities that achieved 100% consensus agreement were: providing access to well-trained healthcare providers in ADHD (mean score 4.74, 95% CI 4.65-4.84) and access to ADHD-related services (mean score 4.50, 95% CI 4.39-4.61). Among the top ten consensus-derived items, the highest frequency pertained to providing access to healthcare experts in ADHD and related-services (50%) followed by research into ADHD on socio-emotional functioning, co-existing conditions and in diagnosing ADHD in females (30%). Increasing knowledge and educating healthcare professionals and school systems on ADHD was also identified among the top ten priorities (20%). CONCLUSIONS: Healthcare professionals identified ten top priorities by consensus where most focused on providing access to trained healthcare providers and services to support ADHD care in Canada. Implementing strategies to improve access on a national level will improve the quality of life for individuals living with ADHD.
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 | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".