Understanding attention deficit/hyperactivity disorder in physicians: workplace implications and management strategies
Bibliographic record
Abstract
Physicians with attention deficit/hyperactivity disorder (ADHD) may have unrecognized workplace difficulties because of inattention and impulsivity. If these behaviours interfere with patient care or organizational functioning, leaders may erroneously attribute the physician’s actions to unprofessionalism. As such, corrective efforts with punitive measures may be ineffective. ADHD is a neurodevelopmental disorder that responds to evidence-based treatments, including medications, accommodations, and supports. Physician leaders who understand the unique presentations of ADHD in physicians may better identify when this condition may be contributing to workplace behaviour. Furthermore, physician leaders may have a professional or legal duty to accommodate or support physicians with underlying medical and/or psychiatric conditions, such as ADHD. Using our own clinical experience, we provide a general overview of ADHD in physicians and guide physician leaders on how to help physicians who may be struggling with ADHD in the workplace. We hope that our clinical experience and observations of this hidden problem will spur discussion, awareness, and action for further research and support.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".