The development and validation of the senior alcohol misuse indicator (SAMI) tool
Bibliographic record
Abstract
Senior alcohol misuse can lead to adverse effects, but remains under-detected due to inadequate screening tools. The objective of this Canada-wide study was to develop and evaluate a brief screening tool, the Senior Alcohol Misuse Indicator (SAMI) to elicit alcohol-related information from seniors. A preliminary form of SAMI was prepared and a focus group of health care professionals (n = 11) was organized to obtain feedback followed by field-testing (n = 158). After SAMI was finalized, it was validated using a follow-up interview based on the Structured Clinical Interview for DSM-IV to determine problem and at-risk drinkers. Ten problem drinkers and 43 at-risk drinkers were identified from the interviews (n = 91). The sensitivity and specificity of SAMI were 78.8% and 55.3%, respectively, and the Area Under the Receiver Operating Curve was 0.706. The SAMI is effective in engaging seniors to talk about their alcohol use and may flag seniors at risk for alcohol-related problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".