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Record W2059167765 · doi:10.1044/ffd24.1.26

European Clinical Specialization in Fluency Disorders (ECSF): Participants Review the First Four Years

2014· article· en· W2059167765 on OpenAlexaff
Margaret M. Leahy, Joseph Agius, Carl Hylebos, Luc De Nil, Kurt Eggers

Bibliographic record

VenuePerspectives on Fluency and Fluency Disorders · 2014
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFluencyPsychologyMedical educationExcellenceQualitative propertyQualitative researchComputer-assisted web interviewingApplied psychologyMedicineMathematics educationPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.040
GPT teacher head0.370
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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