Can cognitive inflexibility reduce symptoms of anxiety and depression? Promoting the structural nested mean model in psychotherapy research
Bibliographic record
Abstract
OBJECTIVE: To estimate the causal effect of executive functioning on the remission of depression and anxiety symptoms in an observational dataset from a vocational rehabilitation program. It is also an aim to promote a method from the causal inference literature and to illustrate its value in this setting. METHOD: With longitudinal (four-time points over 13 months) data from four independent sites, we compiled a dataset with 390 participants. At each time point, participants were tested on executive function and self-reported symptoms of anxiety and depression. We used g-estimation to evaluate whether objectively tested cognitive flexibility affected depressive/anxious symptoms and tested for moderation. Multiple imputations were used to handle missing data. RESULTS: The g-estimation showed a strong causal effect of cognitive inflexibility reducing depression and anxiety and modified by education level. In a counterfactual framework, a hypothetical intervention that could lower cognitive flexibility seemed to cause improvement in mental distress at the subsequent time-point (negative sign) for low education. The less flexibility, the larger improvement. For high education, the same but weaker effect was found, with a change in sign, negative during the intervention and positive during follow-up. DISCUSSION: An unexpected and strong effect was found from cognitive inflexibility on symptom improvement. This study demonstrates how to estimate causal psychological effects with standard software in an observational dataset with substantial missing and shows the value of such methods.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".