Word Recall: Cognitive Performance Within Internet Surveys
Bibliographic record
Abstract
BACKGROUND: The use of online surveys for data collection has increased exponentially, yet it is often unclear whether interview-based cognitive assessments (such as face-to-face or telephonic word recall tasks) can be adapted for use in application-based research settings. OBJECTIVE: The objective of the current study was to compare and characterize the results of online word recall tasks to those of the Health and Retirement Study (HRS) and determine the feasibility and reliability of incorporating word recall tasks into application-based cognitive assessments. METHODS: The results of the online immediate and delayed word recall assessment, included within the Women's Health and Valuation (WHV) study, were compared to the results of the immediate and delayed recall tasks of Waves 5-11 (2000-2012) of the HRS. RESULTS: Performance on the WHV immediate and delayed tasks demonstrated strong concordance with performance on the HRS tasks (ρc=.79, 95% CI 0.67-0.91), despite significant differences between study populations (P<.001) and study design. Sociodemographic characteristics and self-reported memory demonstrated similar relationships with performance on both the HRS and WHV tasks. CONCLUSIONS: The key finding of this study is that the HRS word recall tasks performed similarly when used as an online cognitive assessment in the WHV. Online administration of cognitive tests, which has the potential to significantly reduce participant and administrative burden, should be considered in future research studies and health assessments.
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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 | 0.027 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".