European Clinical Specialization in Fluency Disorders (ECSF): Participants Review the First Four Years
Bibliographic record
Abstract
Background: The European Clinical Specialization in Fluency Disorders (ECSF) is a 1-year postgraduate course for speech and language therapists (known as speech-language pathologists in the United States). The program was developed by a consortium whose members are specialists in fluency disorders from European universities/colleges. The consortium expanded to include other EU college members and specialists from EU centers of clinical excellence. Purpose: This paper presents an evaluative review by students and teachers who have participated in the initial 4 years of ECSF courses. Methodology: Two mixed methods online survey questionnaire were used, one for each group (student course participants and consortium members, designated as teachers throughout the paper) with quantitative, comparative data gathering, along with elements of qualitative data emerging from invited comments, and from open-ended questions. Results: High and relatively high levels of satisfaction were expressed by all participants regarding the overall experience of ECSF. There was a wider range of satisfaction expressed by student participants regarding aspects of course content and experience of clinical work. Participants' responses providing qualitative data indicate major influences of the ECSF on professional development, and strong appreciation of participation in ECSF.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".