Attitudes of Community Developmental Services Agency Staff Toward Issues of Inclusion for Individuals With Intellectual Disabilities
Bibliographic record
Abstract
ABSTRACT In many countries, the shift in policy surrounding intellectual disabilities (ID) from segregation to inclusion has resulted in the closure of large‐scale institutions in favor of integrated community programs and living accommodations. Because the success of the community inclusion movement lies in the hands of the staff who implement these programs, it is important to determine the consistency of their attitudes with the philosophies that underlie policy directions. Using the Community Living Attitudes Scale‐Mental Retardation, Short Form, the current study describes the attitudes of 241 staff working in the field of ID in Ontario, Canada, and examines demographic characteristics that are related to differences in their attitudes toward inclusion. The study found that male staff members' attitudes were less supportive of inclusion than were those of female staff. Moreover, staff members with only a high school education were less likely than those with university or college training to think that individuals with ID were similar to themselves. Finally, older staff members were more likely than younger staff to think that persons with ID should be sheltered from harm. The study showed that many community agency staff members do not hold attitudes that are entirely consistent with the inclusion philosophy and that differences in attitude are associated with their demographic characteristics. This study highlights the need for education and training targeted at particular “at‐risk” staff groups in order to ensure the successful implementation of the goals of the inclusion movement.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".