EMPLOYMENT OF INDIVIDUALS WITH NEURODEVELOPMENTAL DISORDERS: A SCOPING REVIEW OF CONTEXTUAL FACTORS
Bibliographic record
Abstract
Background: Individuals with neurodevelopmental disorders are unemployed or underemployed at staggering rates. Employment for this population is impacted by many factors, including contextual issues. This review was conducted to enhance understanding of contextual factors influencing employment procurement for individuals with neurodevelopmental disorders. Methodology: The Arksey and O’Malley scoping review framework was utilized to examine five databases and sources of grey literature regarding the contextual factors influencing employment procurement for individuals with neurodevelopmental disorders. Articles were analyzed using the International Classification of Functioning, Disability and Health criteria for Contextual Factors, including both Environmental and Personal Factors. Results: The findings from 41 articles indicate that Contextual Factors, Environmental Factors and Personal Factors influence employment procurement by creating both barriers and facilitators to obtaining employment for individuals with neurodevelopmental disorders. Conclusion: A focus on contextual factors that impact individuals with neurodevelopmental disorders may provide further insight into the facilitators and barriers influencing employment outcomes. Further research should aim to understand the strength of relationships and to expand the use and application of the International Classification of Functioning, Disability and Health’s biopsychosocial framework. This research can aid in promoting the employment outcomes for individuals with neurodevelopmental disorders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".