Attitudes Toward Employees with Disabilities: A Systematic Review of Self-Report Measures
Bibliographic record
Abstract
Accurate measures of changes in workplace attitudes toward people with disabilities are required to determine whether employee training and other interventions are effective. This critical systematic review searched Medline, PsycInfo, Google Scholar, Cinahl, and Cochrane Collaboration for suitable instruments published between 2005 and 2015, and for those published earlier if still indicated to be in active use. In total, 13 scans were conducted. Inclusion criteria included wildcard and free text variations ofworkplace attitudes,adults with disabilities, andcompetitive employment. In total, 9 of 49 studies were selected for review. Data from each of these were categorized through the PICO model (Population,Intervention,Comparison, andOutcome), mapped within a PRISMA flow chart, and analyzed through an 18-point weight of evidence framework for empirical quality, relevance, and evidence of theoretical validity. Weight of evidence scores for empirical quality ranged from 10 to 16 out of a possible score of 18. None of the studies provided an explicit evidence of theoretical validity. Measures of responsiveness to change in workplace attitudes appear less well validated than those for single timeframes.
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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".