The experience of speech recognition software abandonment by adolescents with physical disabilities
Bibliographic record
Abstract
Introduction: There is a high rate of speech recognition software (SRS) abandonment by adolescent students with physical disabilities. Purpose: This study sought to describe the experience of adolescents & their parents, who experienced abandonment of SRS. Methods: Using a narrative inquiry method, semi-structured interviews were conducted with three adolescents with a physical disability (and two parents). The individual narratives were transcribed and analyzed using plot-solution and three-dimensional space narrative elements. Results: Participants’ descriptions of their experiences of abandonment emerged along four descriptive themes: (a) they didn’t tell me the whole story, (b) I know how to use it, it’s just not worth the time and effort, (c) it’s just not the right fit for me or my needs, (d) there’s an easier way! Conclusion: Participants believed the SRS was not an adequate fit for their needs or their specific disabilities and so resorted to alternative methods of written communication. A better understanding of the compatibility of the client’s needs with the strengths & limitations of the technology, may improve the prescription and intervention process for both therapists & their clients. Implications for RehabilitationSpeech recognition software (SRS) may be a useful option for those who have difficulty in handwriting.Prescribing clinicians need to clearly outline any potential challenges or limitations of the technology they are recommending in order to help create realistic expectations. They should encourage the users to give honest feedback about their challenges.SRS may not be useful for every type of writing activity and many writing options should be made available to the individual so that the person may choose the option that best fits the activity. Parents and other facilitators, as well as the users themselves need to be helped to understand this.Prescribing clinicians should be aware that SRS in French may have some unique challenges related to the language and the clients’ regional accents.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".