Knowledge and attitudes about Attention-Deficit Hyperactivity Disorder (ADHD): A comparison between practicing teachers and undergraduate education students
Bibliographic record
Abstract
The knowledge and attitudes of practicing teachers regarding ADHD were compared with those of undergraduate education students. Key elements of studies of American and Canadian teachers by Jerome, Gordon, and Hustler (1994) and Jerome, Washington, Laine, and Segal (1999) were replicated. Information was gathered about participants' demographic background (training in ADHD), attitudes towards ADHD, and knowledge about its diagnosis and treatment. Results confirmed the existence of some knowledge gaps, although both practicing teachers and undergraduate education students possessed sound information about ADHD. Misconceptions about ADHD primarily concerned dietary treatment. Attitudes and knowledge were significantly correlated and most participants regarded ADHD as a valid diagnosis with implications for the school setting, and expressed a desire for comprehensive training. Despite similar results for both samples, teachers achieved higher accuracy on knowledge-based questions. These results are discrepant from those of Jerome et al. (1999) who found teachers and students to be similar in factual knowledge. Implications of these findings for curriculum development in academia and in-service teacher training are highlighted.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".