Relationship between diagnostic criteria, depressive equivalents and diagnosis of depression among older adults with intellectual disability
Bibliographic record
Abstract
BACKGROUND: Depression is more common among persons with an intellectual disability (ID) than the general population, and may be expected to increase with age just as in the general population. However, little is known about depression among older adults with ID. The literature has questioned the use of standard diagnostic criteria for depression among both older adults and persons with ID, and behavioural depressive equivalents have been suggested. This study uses the interRAI ID assessment instrument to investigate the relationship between standard diagnostic criteria for depression, depressive equivalents and a diagnosis of depression among older and younger adults with ID in community and institutional settings in Ontario, Canada. METHOD: Items in the interRAI ID assessment instrument that were representative of The Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV) criteria and depressive equivalents were examined among persons with ID in institutional (census-level data) and in community-based (sample) residential settings. Bivariate logistic regression was used to examine the relationship between depressive symptoms and a diagnosis of depression. Descriptive statistics were used to examine the prevalence of depressive symptoms among those who did not have a diagnosis of depression. RESULTS: The results indicate that DSM-IV diagnostic criteria and depressive equivalents were significantly related to a diagnosis of depression among older and younger adults with ID, and that both types of symptoms were exhibited by a non-trivial proportion of individuals without a diagnosis of depression. CONCLUSIONS: The depression rating scale embedded in the interRAI ID is helpful in identifying older adults at risk of depression. Contrary to other studies, few significant differences were found in depressive symptoms by age.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".