The assessment of fidelity in a motor speech-treatment approach
Bibliographic record
Abstract
OBJECTIVE: To demonstrate the application of the constructs of treatment fidelity for research and clinical practice for motor speech disorders, using the Prompts for Restructuring Oral Muscular Phonetic Targets (PROMPT) Fidelity Measure (PFM). Treatment fidelity refers to a set of procedures used to monitor and improve the validity and reliability of behavioral intervention. While the concept of treatment fidelity has been emphasized in medical and allied health sciences, documentation of procedures for the systematic evaluation of treatment fidelity in Speech-Language Pathology is sparse. METHODS: The development and iterative process to improve the PFM, is discussed. Further, the PFM is evaluated against recommended measurement strategies documented in the literature. This includes evaluating the appropriateness of goals and objectives; and the training of speech-language pathologists, using direct and indirect procedures. Three expert raters scored the PFM to examine inter-rater reliability. RESULTS: Three raters, blinded to each other's scores, completed fidelity ratings on three separate occasions. Inter-rater reliability, using Krippendorff's Alpha, was >80% for the PFM on the final scoring occasion. This indicates strong inter-rater reliability. CONCLUSION: The development of fidelity measures for the training of service providers and treatment delivery is important in specialized treatment approaches where certain 'active ingredients' (e.g. specific treatment targets and therapeutic techniques) must be present in order for treatment to be effective. The PFM reflects evidence-based practice by integrating treatment delivery and clinical skill as a single quantifiable metric. PFM enables researchers and clinicians to objectively measure treatment outcomes within the PROMPT approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".