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Record W3118717418 · doi:10.60059/spl.2020.1.92-104

Телепрактиката и биофийдбек методът в помощ на лице, което заеква

2025· article· bg· W3118717418 on OpenAlexaboutno aff
Елка Горанова

Bibliographic record

VenueСпециална педагогика и логопедия · 2025
Typearticle
Languagebg
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsBluetoothSet (abstract data type)MedicineComputer scienceTelecommunicationsProgramming languageWireless

Abstract

fetched live from OpenAlex

Телепрактиката (Telepractice) е широко използвана в медицината на развитите страни за предоставяне на здравни услуги като цяло (Balas et al., 1997), и по-специално за провеждане на консултация, диагностика и терапия при езиково-говорната патлогия (Mashima и Doarn, 2008; Theodoros, 2008). Целта на представеното в статията изследване е да се проучат възможностите за провеждане на дистанционен биофийдбек тренинг при терапия на лице със заекване със сензор за домашна употреба "iFeel Bluetooth HRV" под супервизията на терапевт. Представен е единичен случай на лице със заекване. В терапията е използвана специализирана апаратура за биологична обратна връзка (Биофийдбек) (Vezenkov, 2010; 2011) и техниката за пред-блокажна корекция (Preparatory set), която се използва за модифициране на заекването в момента на случването му (Ward, 2006). Терапията преминава през 6 фази: диагностика, супервизия, Биофийдбек тренинг, дихателен тренинг с включване на говорни задачи, трансфер на новите умения в говорна среда, и последваща диагностика. Първичните резултати показват 30% намаление на честотата на нарушените плавности веднага след провеждане на терапията, с което степента на тежест на заекването е намалена от тежка към лека. При средната продължителност на трите най-дълги спазъма и съпътстващите движения също се отчита подобрение в степента на тежест с 40%. Тези предварителни данни предполагат, че използването на Biofeedback метода в логопедичната практика има потенциала да осигури успешното провеждане на терапия за клиенти с нарушена плавност на речта, прилагайки Телепрактиката. Библиография: Бояджиева-Делева, Е. (2015). Сборник формуляри за логопедичната практика. Помагало за студенти по логопедия. София: УИ "Св. Климент Охридски". Везенков, С. Р. (2011). Приложна неврофизиология на човека. ЕЕГ фийдбек и биофийдбек. Благоевград: УИ „Неофит Рилски“. Везенков, С. Р., Горанова, Е. Г. (2013). Соматична поведенческа терапия. Биофийдбек – парадигми, проблеми, приложения, ефикасност. София: Издателство Neofeedback. Горанова, Е. Г. (2016). Модел за комплексно логопедично и функционално изследване, идентифициране и диференциране на видове и подвидове плавностни нарушения на речта. Докторска дисертация, ЮЗУ "Н. Рилски", Благоевград. http://rd.swu.bg/media/46550/avtoreferat.pdf. Симонска, М. (2013). Диагностика на заекване в предучилищна възраст (методическо ръководство). Благоевград: Издателство „БОН”. American Speech – Language - Hearing Association. (2019). Telepractice: Overview. https://www.asha.org/Practice-Portal/Professional-Issues/Telepractice/. Balas, E.A ., Jaffrey, F., Kuperman, J., & Boren, A. (1997). Electronic communication with patients: evaluation of distance medicine technology. JAMA. 278(2), 152-159. Carey, B., O’Brian, S., Onslow, M., Block, S., Packman, A. & Jones, M. (2010). Randomized controlled non-inferiority trial of a telehealth treatment for chronic stuttering: The Camperdown Program. International Journal of Language and Communication Disorders, 45, 108–120. Duffy, J.R., Werven, G.W. & Aronson, A. E. (1997). Telemedicine and the diagnosis of speech and language disorders. Mayo Clinic Proceedings. 72(2), 1116–222. doi: 10.4065/72.12.111. Harrison, E., Wilson, L. & Onslow, M. (1999). Distance intervention for early stuttering with the Lidcombe Program. Advances in Speech Language Pathology, 1, 31-36. Hill, A. & Theodoros, D. (2002). Research into telehealth applications in speech-language pathology. Journal of Telemedicine and Telecare, 8(4), 187-96. Kully, D. (2000). Telehealth in speech pathology: Applications to the treatment of stuttering. Journal of Telemedicine and Telecare, 6, S39–S41. Kully, D. (2002). Venturing into telehealth: Applying interactive technologies to stuttering treatment. ASHA Leader, 7, 6–7. Language, and Hearing Research, 51, 184–195. Lewis, C., Packman, A., Onslow, M., Simpson, J. & Jones, M. (2008). A Phase II trial of telehealth delivery of the Lidcombe Program of Early Stuttering. American Journal Speech Language Pathology, 17 (2), 139-49. Lowe, R., O'Brian, S., & Onslow, M. (2014). Review of Telehealth Stuttering Management. Folia Phoniatr Logop, 65, 223-238. Manning, W. H. (2000). Clinical Decision Making in Fluency Disorders. Diego, California, Second Edition. Vancouver, Canada: Singular, 114. Mashima, P. A., Doarn, C. R. (2008). Overview of telehealth activities in speech-language pathology. Telemedicine and e-Health, 14, 1101–1117. McGill, M., Noureal, N., & Siegel, J. (2018). Telepractice Treatment of Stuttering: A Systematic Review. Telemedicine Journal and e-Health, 25(5), 359-368. O’Brian, S., Onslow, M., Cream, A. & Packman, A. (2003). The Camperdown Program: Outcomes of a new prolonged-speech treatment model. Journal of Speech, Language, and Hearing Research, 46, 933–946. O’Brian, S., Packman, A. & Onslow, M. (2008). Telehealth delivery of the Camperdown Program for adults who stutter: A Phase I trial. Journal of Speech, Language, and Hearing Research, 51, 184–195. Riley, G.D. (2009). Stuttering severity instrument for children and adults (SSI-4) 4th ed. Pro-Ed, Inc; Austin, TX: Pro-Ed. Sicotte, C., Lehoux, P., Fortier-Blanc, J. & Leblanc, Y. (2003). Feasibility and outcome evaluation of a telemedicine application in speech-language pathology. Speech, Language, and Hearing Research, 46, 933–946. Theodoros, D. G. (2008). Telerehabilitation for Service Delivery in Speech-Language Pathology. J Telemed Telecare, 14(5), 221-224. Ward, D. (2006). Stuttering and Cluttering. Frameworks for understanding and treatment. New York: Psychology press. Wilson, L., Onslow, M. & Lincoln, M. (2004). Telehealth adaptation of the Lidcombe Program of Early Stuttering Intervention: Five case studies. American Journal of Speech-Language Pathology, 13, 81–93.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.008
Scholarly communication0.0110.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.005

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 head0.005
GPT teacher head0.317
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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".

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Published2025
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