The Prevalence of Impairments and Disabilities in the North West Region, Cameroon
Bibliographic record
Abstract
Purpose : This project addressed the lack of disability prevalence data in the North West Region of Cameroon. Methods : A multi-stage cluster design was used, and included urban, semi-urban and rural areas. In the first stage, the team screened 3,933 households, representing an estimated screening sample of 18, 878 individuals. In the second stage, structured interviews were conducted. The interviews included the International Classification of Functioning, Disability and Health (ICF) Checklist to confirm disability status and determine the nature of disability. Results : A total sample of 1,233 individuals screened positive for having a disability. According to this study’s cluster design the prevalence of screening positive for disability in this region was 6.9% (95% CLs 5.7-8.2%) and the population prevalence of disability was 6.2% (95% CLs 5.2-7.2%). Of the individuals who screened positive for disability, 1,106 (89.7%) of them had a participation restriction or activity limitation which was of moderate severity or greater, suggesting that the screening tool was useful for identifying many persons living with moderate or severe disability but not very sensitive at identifying people with minor disabilities. Conclusions : Although certain limitations in the study’s methods must be taken into account, these results can be used to justify the need for, and inform the design of, programming for individuals with disabilities in this region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".