Systematic review of neurocognition and occupational functioning in major depressive disorder
Bibliographic record
Abstract
Occupational impairment accounts for much of the burden and economic costs associated with major depressive disorder (MDD). Many studies have documented neurocognitive deficits in MDD, and depression-associated cognitive dysfunction would be expected to have significant effects on occupational functioning. We systematically reviewed the literature for studies on neurocognition and occupational functioning in MDD. Electronic databases (e.g., MEDLINE, PsychInfo and Cochrane Clinical Trials) were searched using appropriate terms and bibliographies of relevant publications were scanned for additional citations. Two reviewers independently reviewed papers for inclusion and data extraction, with conflicts resolved by consensus. Inclusion criteria were diagnosis of MDD using validated criteria (e.g., DSMâÂÂIV or ICDâÂÂ10), use of objective neuropsychological tests and use of a specific measure of occupational functioning. Of 630 citations identified in the initial search, only two studies met inclusion criteria and were included in a qualitative review. Both had significant methodological limitations. Nonetheless, the depressed samples had significant neurocognitive deficits that were associated with employment status and work impairment. Neurocognitive dysfunction is probably associated with impairment in occupational functioning in individuals with MDD, but the evidence is limited. Further research should examine specific cognitive domains, and use validated measures of work functioning and productivity.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".