Evaluating disability awareness programs in schools: a scoping review of longitudinal outcomes and measures
Bibliographic record
Abstract
PURPOSE: This scoping review examined the measures used, outcomes assessed, and the longitudinal impacts of disability awareness programs. MATERIALS AND METHODS: Studies were identified from September 2011 to June 2023 across seven electronic databases. Covidence review software and Microsoft Excel were used to manage data. The data analysis included frequency counts of measures used and categorisation of the types of outcomes assessed. The longitudinal outcomes were synthesised according to the outcomes measured. RESULTS: Seventy-two studies were included from 26 countries, utilising a range of measures to assess cognitive, affective, and behavioural outcomes of programs. A subset of 14 longitudinal studies was identified to explore longer-term outcomes, from 1 to 30-month follow-up. All four longitudinal studies assessing cognitive outcomes demonstrated sustained increases. Ten of 11 studies that assessed changes in attitudes generally found sustained improvements, while only one of five studies found lasting improvements on behaviours. CONCLUSIONS: Disability awareness programs can be an effective approach to increase knowledge, improve attitudes and can, to some extent, increase inclusive behaviours of students towards peers with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".