Quantification of Meta-Therapy in Conversation Training Therapy
Bibliographic record
Abstract
PURPOSE: Meta-therapy (MT) is a clinical entity that plays a pivotal role in the success of voice therapy. This descriptive study is the first empirical research to describe and quantify the use of MT in comparison to other treatment modalities during conversation training therapy (CTT). METHOD: Twenty-four prerecorded CTT sessions featuring six voice-specialized speech-language pathologists (SLPs) were converted into audio files and transcribed into text files. Trained annotators (experienced and novice voice-specialized SLPs) identified the use of MT and four other treatment modalities (direct treatment, education and indirect treatment, and counseling) during the therapy sessions. Descriptive statistics and intra- and inter-annotator agreements (Cohen's kappa) were generated. RESULTS: MT was used systematically throughout the course of CTT, both as a stand-alone therapeutic approach and in conjunction with other treatment modalities. When blended with other treatment modalities, MT accounted, on average, for 31% of all clinical dialogues within the course of treatment. Intra-annotation reliability was generally high. However, inter-annotator reliability was notably lower and did not differ between novice and experienced SLPs, not only for MT but also for the other four treatment modalities. CONCLUSIONS: This study underscores the multifaceted nature of MT and supports the notion that it is a core element of voice therapy. We propose that MT could be formally taught to future SLPs alongside other clinical components (e.g., indirect treatment, direct treatment, and counseling). This study also highlights the need for standardization and operationalization of all key components of voice therapy. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29405753.
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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 | 0.059 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".