A systematic review of programs and interventions for increasing the interest and participation of children and youth with disabilities in STEM education or careers
Bibliographic record
Abstract
Background: Individuals with disabilities are under-represented in STEM education and careers. Research from the US has examined interventions for increasing the interest or participation of youth with disabilities in STEM, but this work has not yet been synthesized.Objective: We reviewed and critically appraised the literature on the impact of programs and interventions for increasing the interest and participation of children and youth with disabilities in STEM education and careers.Methods: We conducted a systematic review of 5 databases (CINAHL, ERIC, MEDLINE, PsychINFO, and Scopus) for peer-reviewed articles published between 1993 and June 2018 reporting a STEM intervention. Risk of bias across studies and the overall rigour of the studies were assessed with the American Academy of Neurology guideline, and risk of bias within studies was assessed with Kmet and colleague’s (2004) standard quality assessment.Results: We identified 17 articles meeting our inclusion criteria. All studies originated from the United States, representing 953 participants, ranging from 9 to 23 years old. Seventy-eight percent of the quantitative studies reported significant improvement in at least one of the following: perceived self-advocacy, self-esteem, social skills, independence, perceived value of the intervention, preparation for college and employment, perceived career options, or interest in taking STEM classes and pursuing STEM careers. Positive outcomes were reported across many delivery formats, including web-based interventions, virtual and face-to-face mentoring programs, and course-based interventions or workshops.Conclusion: More research, with more rigorous, controlled designs, is needed to determine the impact of specific intervention components, and participant characteristics, such as gender, on intervention effectiveness.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".