Specificity of Future Thinking in Depression: A Meta-Analysis
Bibliographic record
Abstract
Reduced specificity of autobiographical memory has been well established in depression, but whether this overgenerality extends to future thinking has not been the focus of a meta-analysis. Following a preregistered protocol, we searched six electronic databases, Google Scholar, and personal libraries and contacted authors in the field for studies matching search terms related to depression, future thinking, and specificity. We reduced an initial 7,332 results to 46 included studies, with 89 effect sizes and 4,813 total participants. Random-effects meta-analytic modeling revealed a small but robust correlation between reduced future specificity and higher levels of depression ( r = −.13, p < .001). Of the 11 moderator variables examined, the most striking effects were related to the emotional valence of future thinking ( p < .001) and the sex of participants ( p = .025). Namely, depression was linked to reduced specificity for positive (but not negative or neutral) future thinking, and the relationship was stronger in samples with a higher proportion of males. This meta-analysis contributes to our understanding of how prospection is altered in depression and dysphoria and, by revealing areas where current evidence is inconclusive, highlights key avenues for future research.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".