Impact of work type and APOE-e4 status on cognitive functioning in older women
Bibliographic record
Abstract
Prior research indicates that APOE-e4 allele(s) and working without compensation may be independently associated with risk for cognitive decline. This study investigated whether the interaction of type of work (paid versus unpaid) and presence of APOE-e4 allele(s) was associated with cognitive dysfunction in women in mid- and late-life. Participants included 340 females (mean age = 74.7 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. A two-way ANOVA to assess the simple main effects of type of work and APOE-e4 allele status on cognition as well as their interaction was performed. A two-way ANCOVA including age, education, and marital status as covariates was also conducted. The presence of one or two APOE-e4 allele(s) and unpaid work was associated with greater cognitive dysfunction. A significant interaction effect revealed engagement in paid work, regardless of the presence of APOE-e4 allele(s), was associated with better cognitive functioning. Consistent with prior literature, women who engage in unpaid forms of labor for the majority of their life may be at higher risk for cognitive decline, regardless of presence of APOE-e4 allele(s). Further research is needed to identify the factors related to unpaid labor that may increase risk for cognitive dysfunction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".