Measuring working memory in contexts of high adversity: Using the digit span in North Kivu
Bibliographic record
Abstract
Having good measures of executive functions in general and of working memory (WM) in particular is very important, as these cognitive functions are associated with numerous consequential outcomes. Because few studies have examined WM in underprivileged populations exposed to high levels of adversity, including chronic armed conflicts and frequent natural disasters, the relevance of WM assessments in this context is still unknown. Our general objective was to examine the usefulness and usability of simple digit span measures of WM in contexts of high adversity, in North Kivu, in the eastern Democratic Republic of Congo, specifically by investigating links with formal schooling and other cognitive measures, as well as test-retest reliability. We conducted two studies. The first study included 97 internally displaced participants recently exposed to an upsurge in armed conflicts. In the second study, 281 participants were tested shortly after the eruption of the Nyaragongo volcano, and 115 of them were tested again 8 weeks after. We used the forward digit span, backward digit span and instruments measuring functional impairments and everyday cognitive skills. Performance on the digit span was associated with the level of schooling as well as everyday mathematical problem-solving and self-reported functional cognitive impairments. The results also showed a relatively strong stability of the forward digit span scores over an 8-week period. Contribution: Results of these studies support the usefulness of a simple digit span measure to study one important aspect of executive function in contexts of high adversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".