Everyday life memory deficits in pregnant women.
Bibliographic record
Abstract
Converging evidence indicates that pregnant women report experiencing problems with memory, but the results of studies using objective measures are ambiguous. The present study investigated potential reason(s) for the discrepancy between findings of subjective and objective memory deficits, as well as potential source(s) of pregnant women's problems with memory. Sixty-one pregnant and 24 nonpregnant women completed a series of memory tests which included field and laboratory measures of prospective memory. Three standardized questionnaires were used to assess subjective aspects of memory. The influence of cortisol, depressed mood, anxiety, physical symptoms, sleep/fatigue, and busyness on pregnancy-related deficits was also examined. The findings revealed objective pregnancy-related deficits on two of the field measures of prospective memory. Pregnancy-related subjective deficits were also detected on all of the questionnaires. In contrast, no objective pregnancy-related deficits were found on the laboratory measures of memory. Increased physical symptoms accounted for one of the objective deficits in memory, while depressed mood and physical symptoms accounted for two of the subjective memory deficits. Collectively, these findings suggest that pregnant women experience everyday life problems with memory that are not readily detected in the laboratory environment. The predominant use of laboratory tests may explain the myriad of previous failures to detect objective deficits in pregnant women's memory.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".