Counseling People with Disabilities
Bibliographic record
Abstract
OVERVIEW. The counseling needs of people with disabilities in African settings have received relatively little attention in the published literature, despite the fact that a majority of Africans, or their families, experience disability in their lifetimes. The disabilities arise mostly from avoidable causes such as inadequate health care systems, civil strife, marginal or failing national economies, and lack of enforcement of disability rights by national governments. This chapter addresses conceptions of disability from an African cultural heritage perspective and counseling interventions to address disability-related needs in those settings. Legal and professional issues that influence the availability of psychosocial services to people with disabilities in sub-Saharan Africa are considered. Counseling enhances health and quality of life in people of African descent with disabilities. LEARNING OBJECTIVES By the end of the chapter, the reader should be able to: Define disability-related terms. Explain the likely influence of culture on disability identity in an African cultural heritage setting. Identify and describe the needs in people of African descent with disabilities that counseling services would meet. Outline the typical counseling interventions that have been used in counseling people with disabilities in African settings and the rationale for their use. Evaluate the role of community-based approaches in counseling people of African descent with disabilities. Suggest and discuss ways in which counseling services for African cultural heritage people with disabilities could be enhanced.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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".