Do older adults respond to cognitive behavioral therapy as well as younger adults? An analysis of a large, multi‐diagnostic, real‐world sample
Bibliographic record
Abstract
OBJECTIVES: Older adults (OA; ≥55 years of age) are underrepresented in patients receiving cognitive-behavioral therapy (CBT). This study evaluates mental health outcomes for OA compared to younger adults (YA; <55 years of age) receiving CBT. DESIGN: This is a pre-post study comparing the effectiveness of CBT for OA (n = 99) and YA (n = 601) in a CBT service located in a university-affiliated tertiary care hospital in Canada. Data was collected between 2001 and 2021. Participants received a mean of 18.5 sessions (SD 10) of standard, evidence-based CBT with treatment integrity checks. The main outcome was clinically significant change, as measured by the Reliable Change Index (RCI). Secondary outcomes were change in the Global Severity Index (GSI-SCL) of the Symptoms Checklist-90 (Revised), and Clinical Global Improvement scores (CGI). RESULTS: The RCI allowed a comparison of treatment efficacy across diagnoses. Both groups experienced similar improvement on the RCI (2.92 [±3.64] vs. 3.15 [±4.86], p = 0.65). Furthermore, 39% of OA and 42% of YA no longer met criteria for their diagnoses. Groups did not differ with respect to changes in the GSI-SCL. The CGI severity comparison suggested that OA had milder illness. In all outcomes (RCI, CGI and GSI-SCL), participants improved over time. CONCLUSIONS: This real-world study analyzed a large sample of OA and YA undergoing CBT for various mental health conditions. Both groups were found to benefit equally.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".