Challenges and opportunities for telehealth assessment during COVID-19: iT-RES, adapting a remote version of the test for rating emotions in speech
Bibliographic record
Abstract
OBJECTIVE: COVID-19 social isolation restrictions have accelerated the need to adapt clinical assessment tools to telemedicine. Remote adaptations are of special importance for populations at risk, e.g. older adults and individuals with chronic medical comorbidities. In response to this urgent clinical and scientific need, we describe a remote adaptation of the T-RES (Oron et al. 2020; IJA), designed to assess the complex processing of spoken emotions, based on identification and integration of the semantics and prosody of spoken sentences. DESIGN: We present iT-RES, an online version of the speech-perception assessment tool, detailing the challenges considered and solution chosen when designing the telehealth tool. We show a preliminary validation of performance against the original lab-based T-RES. STUDY SAMPLE: = 39). RESULTS: i-TRES performance closely followed that of T-RES, with no group differences found in the main trends, identification of emotions, selective attention, and integration. CONCLUSIONS: The design of iT-RES mapped the main challenges for remote auditory assessments, and solutions taken to address them. We hope that this will encourage further efforts for telehealth adaptations of clinical services, to meet the needs of special populations and avoid halting scientific research.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".