A Lexicon of Assessment and Outcome Measures for Telemental Health
Bibliographic record
Abstract
BACKGROUND: The purpose of this document is to provide initial recommendations to telemental health (TMH) professionals for the selection of assessment and outcome measures that best reflect the impacts of mental health treatments delivered via live interactive videoconferencing. MATERIALS AND METHODS: The guidance provided here was created through an expert consensus process and is in the form of a lexicon focused on identified key TMH outcomes. RESULTS: Each lexical item is elucidated by a definition, recommendations for assessment/measurement, and additional commentary on important considerations. The lexicon is not intended as a current literature review of the field, but rather as a resource to foster increased dialogue, critical analysis, and the development of the science of TMH assessment and evaluation. The intent of this lexicon is to better unify the TMH field by providing a resource to researchers, program managers, funders, regulators and others for assessing outcomes. CONCLUSIONS: This document provides overall context for the key aspects of the lexicon.
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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 | 0.094 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".