A Vocal Assessment Method (VAM) to Evaluate the Effectiveness of Interactive Drawing Therapy for Patients with Dementia (PwD) (A Prospective Paper)
Bibliographic record
Abstract
Abstract Background It is crucial to develop non‐pharmacological interventions to help people with dementia. Among different types of non‐pharmaceutical interventions for dementia, art therapy interventions have been shown helpful for improving patients’ physiological and mental states, in particular their cognitive performances, cognitive functionalities, including emotional states, verbal fluency, and behavioral reactions. We use Interactive Drawing Therapy (IDT), proposed by Russell Withers, as an art therapeutic method for people with dementia and to explore the method’s effectiveness, we suggest developing a vocal assessment method. Method We will recruit participants with mild AD and individuals without dementia and divide them into two groups, an experimental group that will attend the IDT sessions and a control group that won’t be offered the IDT sessions. All participants will attend two picture description sessions while they describe the cookie theft picture or the picnic scene. For the experimental group, we will offer attending two IDT sessions between two picture description sessions. During the first IDT session, the art therapist helps participants draw their thoughts about what he is doing during the day and describe their feelings. During the second IDT session, the art therapist shows one of the pictures, “Man changing the bulb” or “The Cat in the Tree,” and asks them to describe the picture with drawing and writing words. We collect speeches of participants during picture description and IDT sessions. The cognitive status of each participant will be measured using the Mini‐mental state examination (MMSE) test. Result We will aim to develop a binary classifier to distinguish patients with improved verbal fluency from patients without any enhancements in verbal fluency. It can be a part of a vocal assessment method to assess improvements in verbal fluency of patients attending IDT sessions which will be designed by combing the picture description. We also expect that such IDT sessions can enhance the verbal fluency of patients with dementia. Conclusion This study will promise to develop a vocal system to discover the effectiveness of IDT for peoplewith dementia. As a part of this study we aim to identify linguistic and acoustic features that might change during the IDT sessions.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".