An Evidence-based Toolset to Capture, Measure and Assess Emotional Health
Bibliographic record
Abstract
We present: (1) an automated telephone check-in system to capture emotional health, based on automatic emotion classification, crowd-sourcing, and the experience sampling method; (2) a method that combines acoustic-based and perception-based emotion classifiers to maximize the likelihood of correctly identifying the emotion in a speech recording; (3) an evidence-based toolkit to measure and assess emotional health; and (4) the results of three experimental trials held in 2010 and 2011: (a) English speaking members of Alcoholics Anonymous, (b) English and French speaking general population, and (c) English speaking Opioid addicts undergoing Suboxone maintenance treatment. Emotional health can be defined as the ability to express emotions, identify one's own emotions, relate to other people's emotions, and to live life with predominantly positive emotions. Emotional health plays a major role in addiction treatment and Cognitive Behavioral Therapy (CBT).
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".