Bibliographic record
Abstract
This study explores the possible uses of Sentence Shaper (Linebarger, McCall, & Berndt, 2004), a software program designed as a language remediation tool for individuals with nonfluent aphasia. In particular, Sentence Shaper allows users to practice message production by facilitating sentence construction. Previous studies indicate that after extended use of this program, individuals with nonfluent aphasia may be able to produce more morphosyntactically complex narratives. As Sentence Shaper allows users to record and save messages, there is the potential that this program could be used to augment communication. The goals of this study were, first, to partially replicate the Linebarger et al. (2004) study and, second, to explore ways in which Sentence Shaper could be used to augment communication in everyday life. These goals were investigated in a four-month case study with a woman with nonfluent aphasia and her mother. Models of social approaches to aphasia intervention informed the design of the study, which included a treatment component and an ethnographic component. For the treatment component, the participant with aphasia practiced producing messages using Sentence Shaper for 12 weeks. Treatment effects were measured by comparing pre- and post-treatment unaided narratives, as well as post-treatment aided narratives. The ethnographic component involved regular meetings with the participants. Integration of qualitative data from fieldnotes, recorded conversations, brief interviews, and questionnaires revealed the uses of Sentence Shaper and the ways in which the participants’ life situation affected its use. Treatment results demonstrated an increase in the morphosyntactic complexity of the participant’s narratives, while measures of informativeness and narrative structure remained relatively unchanged. Given these conflicting findings, a judgment task was also conducted with two groups of listeners (speech-language pathologists and peers). Findings from the ethnographic component revealed that, although the participant with aphasia used Sentence Shaper messages for e-mail and in conversation, neither participant readily accepted the use of the program to augment communication in everyday life. Reasons for this lack of acceptance are explored. Finally, ways in which a social approach contributed to a deeper understanding of the findings, as well as implications for future research and clinical practice, are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".