52 Association of psychotherapy with long-term disability benefit claim closure among patients disabled due to depression
Bibliographic record
Abstract
Objectives To evaluate the effect of psychotherapy for depression in patients receiving disability benefits. Methods Using administrative data from a large Canadian, private, disability insurer, we evaluated the association between the provision of psychotherapy and other potentially predictive factors with time to long-term disability (LTD) claim closure. Results We analysed 10,338 LTD claims in which depression was the primary disabling complaint. Depression management included psychotherapy in 1580 (15.3%) LTD claims. In our adjusted analyses, receipt of psychotherapy was associated with faster claim closure (hazard ratio [HR] = 1.42; 95% confidence interval [CI] = 1.33 to 1.52). Older age per decade (0.83 [0.80 to 0.85] respectively), a primary diagnosis of recurrent depression (0.80 [0.74 to 0.87]) versus major depression, a secondary psychological (0.77 [0.72 to 0.81]), or non-psychological diagnosis (0.66 [0.61 to 0.71]), a longer time to claim approval (0.995 [0.992 to 0.998], and an administrative services only policy (0.87 [0.78 to 0.96] or refund policy (0.73 [0.69 to 0.77]) versus non-refund policy were associated with longer time to claim closure. Residing in the Prairies (1.46 [1.35 to 1.57]) and Quebec (1.93 [1.82 to 2.05]) versus Ontario were associated with faster LTD claim closure. Conclusions We found multiple factors, including psychotherapy, which were predictive of time to LTD claim closure. Our findings may however be influenced by selection bias and other biases that present challenges to the analysis and interpretation of administrative data, and highlight the need for well-designed prospective studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".