Mental Retardation in Teenagers: Prevalence Data from the Niagara Region, Ontario
Bibliographic record
Abstract
OBJECTIVES: There are few Canadian prevalence studies of mental retardation (MR); those that do exist were conducted prior to the era of community integration. We undertook a population-based study to explore mental health disturbances in young persons with MR. The first requirement was to identify a population with MR and to establish its prevalence. Here, we report data on the prevalence of MR in a population aged 14 to 20 years. METHOD: We conducted the study in the Niagara Region of Ontario, which has a population base of around 400,000. Researchers worked closely with schools and with agencies providing services to persons with MR to identify the study group. We confirmed the functioning level of participants through standard tests of nonverbal intelligence and receptive language; teachers and other service personnel provided information relevant to the estimation of nonparticipants' functioning level. RESULTS: We identified 255 individuals as having MR (IQ < or = 75). Of these, 171 chose to participate (defined as "participants with MR"; the remaining 84 were "nonparticipants with MR"). Thus, the participation rate was 67% (171/255). Participants and nonparticipants with MR did not differ on age, sex, or IQ, although there were more nonparticipants in the lower social strata. Overall prevalence for MR was 7.18/1000. For mild mental retardation (MMR; that is, IQ = 50 to 75), prevalence was 3.54/1000, and for severe mental retardation (SMR; that is, IQ < 50), it was 3.64/1000. CONCLUSIONS: Our prevalence estimate for SMR is similar to rates from previous studies conducted worldwide. Our estimate for MMR parallels the lower rates found in Scandinavian countries and contrasts with the higher rates generally reported in the US.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".