Factors influencing discrepancies in self-reported memory and performance on memory recall in the Canadian Community Health Survey—Healthy Aging, 2008–09
Bibliographic record
Abstract
OBJECTIVE: the objectives of this study were: (i) to estimate the rate of discrepancy between participant single-item self-reports of good memory and poor performance on a list-learning task and (ii) to identify the factors including age, gender and health status that influence these discrepant classifications. STUDY DESIGN AND SETTINGS: in total, 14,172 individuals, aged 45-85, were selected from the 2008-09 Canadian Community Health Survey on Healthy Aging. We examined the individual characteristics of participants with and without discrepancies between memory self-reports and performance with a generalised linear model, adjusting for potential covariates. RESULTS: the mean age of respondents was 62.9 years with 56.7% being female, 53.8% having post-secondary graduation and 83% being born in Canada. Higher discrepant classification rates we observed for younger people (6.77 versus 3.65 for lowest and highest group), female (5.90 versus 3.68) and with higher education (6.17 versus 3.52). Discrepant classification rates adjusted with all covariates were higher for those without chronic diseases (5.37 [95% Confidence Interval (CI): 4.16, 6.90] versus 4.05 95% CI: 3.38, 4.86; P = 0.0127), those who did not drink alcohol (5.87 95% CI: 4.69, 7.32 versus 3.70 95% CI: 3.00, 4.55; P < 0.0001), lonely participants (5.45 95% CI: 4.20, 7.04 versus 3.99 95% CI: 3.36, 4.77; P = 0.0081) and bilingual participants (5.67 95% CI: 4.18, 7.64 versus 3.83 95% CI: 3.27, 4.50; P = 0.0102). CONCLUSION: the findings of this study suggest that the self-reported memory and memory performance differ in a substantial proportion of the population. Therefore, relying on a self-reported memory status may not accurately capture those experiencing memory difficulties.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".