Community Integration as Acculturation: Preliminary Validation of the AIMS Interview
Bibliographic record
Abstract
Background This paper introduces the Assimilation, Integration, Marginalization, Segregation (AIMS) interview, a new measure of community integration (defined as acculturation) and reports validation data supporting the use of AIMS with individuals with developmental disabilities. Methods Caregivers acted as informants for 66 adults with moderate‐to‐mild developmental disabilities. All participants were living in the community. Results The data gathered using AIMS provide evidence of sound psychometric properties including content, concurrent and construct validity. AIMS data for participants with developmental disabilities indicated that integration efforts have been relatively successful in the medical, dental, housing, social and community domains; however, education, employment, volunteer and spiritual activity require attention. Conclusions In addition to use as a research tool and outcome measure, AIMS can be used to inform service delivery by providing information regarding the supports available to individuals with disabilities in a number of domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".