Systematic review of the effect of the psychosocial working environment on cognition and dementia
Bibliographic record
Abstract
The high incidence of cognitive impairment in the ageing population, together with the challenges it imposes to health systems, raises the question of what affect working life has on cognitive abilities. The study, therefore, reviews recent work on the longitudinal impact of psychosocial work conditions on cognitive functioning and on dementia. Relevant articles were identified by a systematic literature search in PubMed and PsycINFO using a standardised search string and specific inclusion and exclusion criteria. We included articles reporting longitudinal effects that were investigated in cohort studies, case-control studies or randomised controlled trials in the working population. Two independent reviewers evaluated the studies in three subsequent phases: (i) title-abstract screening, (ii) full-text screening and (iii) checklist-based quality assessment.Methodical evaluation of the identified articles resulted in 17 studies of adequate quality. We found evidence for a protective effect of high job control and high work complexity with people and data on the risk of cognitive decline and dementia. Moreover, cognitively demanding work conditions seem to be associated with a decreased risk of cognitive deterioration in old age.Psychosocial work conditions can have an impact on cognitive functioning and even on the risk of dementia. As the world of work is undergoing fundamental changes, such as accelerated technological advances and an ageing working population, optimising work conditions is essential in order to promote and maintain cognitive abilities into old age.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| 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".