P2‐306: Help‐Seeking for Subjective Memory Complaints (SMC) in Older Adults in New Brunswick
Bibliographic record
Abstract
More than 50% of patients suffering from dementia even do not know that they have dementia. This study explore help-seeking intentions and behaviors for Subjective Memory Complaints (SMC) among community-dwelling older adults with and without current SMCs. It is a Cross-sectional design study in South-Eastern New Brunswick, Canada. using descriptive, correlational and comparative analysis. A total of 105 community-dwelling older adults between the ages of 42 and 90 years old were included in the study. Help-seeking intentions via various sources for future SMCs were explored. Cognitive, physical health and psychological distress measures were included to identify differences between those who had sought help for their current SMC and those who had not. 95% of sample participants indicated intending to seek help from a general physician in the event of future SMCs. Perceptions of current memory performance was associated with intentions to seek help from a general physician and delaying help-seeking for future SMCs. For individuals who currently had SMCs, only 26% had sought help from a health care professional. In spite of the fact that the majority of the sample participants expressed intentions to seek help from a general physician in the event of future SMCs, a large portion of them did not execute formal help-seeking behaviors in the presence of actual SMCs. This help-seeking prevalence rate for SMCs is consistent with other studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".