Employing Strength: A Scoping Review of Customized Employment Practices to Support Inclusive Employment for People with Intellectual Disabilities
Bibliographic record
Abstract
Background Inclusive employment offers advantages for both employers and individuals with intellectual disabilities. However, high unemployment rates persist for people with intellectual disabilities, underscoring the need for alternative approaches. Customized employment (CE) has emerged as a promising strategy by tailoring job opportunities to align with individual strengths and employer requirements. Objective This review answers the question, “What does the literature say about the use of customized employment practices to facilitate paid employment for people with intellectual disabilities?” Methods We conducted a scoping review of the literature. Eight databases were searched, including APA PsycInfo, Medline, CINAHL, Scopus, Web of Science, Business Source Ultimate, Social Services Abstracts and Social Science Abstracts. Results Fifty-seven articles were deemed relevant to the research question, revealing clear trends and key characteristics of CE. The literature suggests that CE can lead to improved employment outcomes, greater self-determination and independence, and increased employer satisfaction. However, lack of evaluative measures has led to inconsistencies in delivery and quality of support. CE practices may demand more time and higher costs compared to other types of supported employment. Conclusions When implemented effectively, CE practices can be a valuable method for supporting individuals with intellectual disabilities in securing inclusive employment.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".