Examining the Utility of the Saskatchewan Mood Inventory for Individuals with Memory Loss
Bibliographic record
Abstract
ABSTRACT The Saskatchewan Mood Inventory (SMI) is a caregiver-focused assessment and research tool that was designed to enhance understanding of the emotional experiences of individuals with dementia and to identify relationships between level of cognitive impairment and family member ratings of pleasant and unpleasant emotional responses during daily activities. Family members were instructed to use the semi-structured written log to document prospectively the type and intensity of emotion expressed by the individual with dementia, to describe the associated emotion-evoking events or activities, and to monitor and record their own emotional reactions. Twenty-seven family caregivers recruited from Alzheimer support groups used the log consistently during a 2-week monitoring period to document an average of three emotion-evoking events per day. Average emotion ratings were more positive for individuals with moderate levels of dementia than for those with severe cognitive impairment, and caregivers' ratings of their family members' and their own emotional states were positively correlated. The event-reporting procedures produced narrative descriptions of emotion-evoking activities that were subsequently coded for content. Inter-rater reliability estimates were high. Event-category summaries are reported in association with positive, negative, and neutral emotional responses for individuals with moderate and severe levels of dementia. Level of impairment was related both to the relative frequency of positive and negative emotions and to the type of event category reported by caregivers.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".