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Record W2604754021 · doi:10.1080/14015439.2017.1307445

Visual stimuli in intervention approaches for pre-schoolers diagnosed with phonological delay

2017· article· en· W2604754021 on OpenAlexaff
Cassandra Pedro, Marisa Lousada, Andreia Hall, Luís M. T. Jesus

Bibliographic record

VenueLogopedics Phoniatrics Vocology · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCoquitlam College
FundersInstituto de Engenharia Eletrónica e Informática de Aveiro, Universidade de Aveiro
KeywordsSpeech soundIntervention (counseling)PsychologyPhonological DisorderPortuguesePhonologySpeech productionAudiologySound (geography)Production (economics)LinguisticsCognitive psychologyComputer scienceSpeech recognitionMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to develop and content validate specific speech and language intervention picture cards: The Letter-Sound (L&S) cards. The present study was also focused on assessing the influence of these cards on letter-sound correspondences and speech sound production. An expert panel of six speech and language therapists analysed and discussed the L&S cards based on several criteria previously established. A Speech and Language Therapist carried out a 6-week therapeutic intervention with a group of seven Portuguese phonologically delayed pre-schoolers aged 5;3 to 6;5. The modified Bland-Altman method revealed good agreement among evaluators, that is the majority of the values was between the agreement limits. Additional outcome measures were collected before and after the therapeutic intervention process. Results indicate that the L&S cards facilitate the acquisition of letter-sound correspondences. Regarding speech sound production, some improvements were also observed at word level. The L&S cards are therefore likely to give phonetic cues, which are crucial for the correct production of therapeutic targets. These visual cues seemed to have helped children with phonological delay develop the above-mentioned skills.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.364
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2017
Admission routes1
Has abstractyes

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