Medicalization, Ambivalence and Social Control: Mothers’ Descriptions of Educators and ADD/ADHD
Bibliographic record
Abstract
Conrad notes that non-medical personnel often accomplish the routine, everyday work of medicalization. This is particularly so in the case of Attention Deficit (Hyperactivity) Disorder, where teachers, special educators and school psychologists identify, assess and administer medication to 'problematic' children. Drawing on data from interviews with Canadian and British mothers of ADD/ADHD children, this article explores mothers' perceptions of educators' roles in medicalizing children who are different, comparing medicalization in two divergent sites. In Canada, where ADD/ADHD is a highly medicalized phenomenon, and teachers have few alternative forms of social control available to them in classrooms, it appears that educators are prepared to identify problem children and press for medical treatment with remarkable vigor. In Britain, where medicalization remains incomplete, and where teachers and special educators have more stringent alternative forms of social control available to them, educators were often described as gatekeepers who will refuse the label or to administer medication.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".