Bibliographic record
Abstract
No AccessPerspectives on Administration and SupervisionArticle1 Oct 2005Evidence-Based Practice in Audiology Deborah S. Culbertson, and Sherri M. Jones Deborah S. Culbertson East Carolina UniversityGreenville, NC Google Scholar More articles by this author and Sherri M. Jones East Carolina UniversityGreenville, NC Google Scholar More articles by this author https://doi.org/10.1044/aas15.3.17 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In References Apel, K. (1999). Checks and balances: Keeping the science in our profession..Language, Speech, and Hearing Services in the Schools, 30, 98–107. ASHAWireGoogle Scholar Apel, K. & Self, T. (2003). Evidence-based practice: The marriage of research and clinical services. Retrieved June 15, 2005, from http://www.asha.org/about/publications/leader-online/archives/2003/q3/030909.htm. Google Scholar ASHA. (2004a). Evidence-based practice in communication disorders: An introduction, from: [Technical Report]. Retrieved April 27, 2005, from: www.asha.org/members/deskref-journals/deskref/default. Google Scholar ASHA. (2004b). Report of the Joint Coordinating Committee on Evidence-Based Practice. Retrieved April 27, 2005, from www.asha.org/members/ebp/doc-rpt.htm Google Scholar ASHA. (2005). Evidence-based practice in communication disorders, from [Position statement]. Retrieved April 27, 2005, from www.asha.org/members/deskref-journals/deskref/default Google Scholar Casby, M. W. (2001). Otitis media and language development: A meta-analysis..American Journal of Speech Language Pathology, 10, 65–80. ASHAWireGoogle Scholar Crandell, C. C., & Smaldino, J.J. (1996). Speech perception in noise by children for whom English is a second language..American Journal of Audiology, 5, 47–51. ASHAWireGoogle Scholar Domitz, D. M., & Schow, R. L. (2000). A new CAPD battery—Multiple auditory processing assessment: Factor analysis and comparisons with SCAN..American Journal of Audiology, 9, (2) 101–111. ASHAWireGoogle Scholar Fey, M. E., & Johnson, B. W. (1998). Research to practice (and back again) in speech-language intervention..Topics in Language Disorders, 18, 23–24. Google Scholar Hawkins, D. B., & Naidoo, S. V. (1993). Comparison of sound quality and clarity with asymmetrical peak clipping and output limiting compression..Journal of the American Academy of Audiology, 4, 221–228. Google Scholar Hodgson, W. R. (1986). Hearing aid assessment and use in audiologic habilitation (3rd ed.). Baltimore: Williams & Wilkins. Google Scholar Johnson, C. E., & Danhauer, J. L. (2002). Handbook of outcomes measurement in audiology.: Canada: Delmar Learning. Google Scholar Keith, R. W. (1986). SCAN: A screening test for auditory processing disorders.: San Antonio, TX: The Psychological Corporation. Google Scholar Ramkissoon, I., Proctor, A., Lansing, C. R., & Bilger, R. C. (2002). Digit speech recognition thresholds (SRT) for non-native speakers of English..American Journal of Audiology, 11, 23–28. ASHAWireGoogle Scholar Robey, R. R., & Dalebout, S.D. (1998). A tutorial on conducting meta-analy-ses of clinical outcome research..Journal of Speech, Language, and Hearing Research, 41, 1227–1241. ASHAWireGoogle Scholar Wiley, T. L., Stoppenback, D. T., Feldhake, L. J., Moss, K. A., & Thordardottir, E. T. (1995). Audiologic practices: What is popular versus what is supported by evidence..American Journal of Audiology, 4, 26–34. ASHAWireGoogle Scholar Additional Resources FiguresReferencesRelatedDetails Volume 15Issue 3October 2005Pages: 17-20 Get Permissions Add to your Mendeley library History Published in issue: Oct 1, 2005 Metrics Topicsasha-topicsasha-sigsasha-article-typesleader-topicsCopyright & Permissions© 2005 American Speech-Language-Hearing AssociationPDF downloadLoading ...
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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 | 0.066 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".