Perceptions of health care workers prescribing augmentative and alternative communication devices to children
Bibliographic record
Abstract
PURPOSE: Access to assistive devices is critical for most children with disabilities to function in society. Despite this, there remain high levels of unmet needs and an underutilisation of augmentative and alternative communication (AAC) devices. Yet, relatively little is known about the challenges that clinicians encounter in prescribing AAC devices. METHOD: In-depth qualitative semi-structured interviews were conducted with 11 speech language pathologists and occupational therapists who are current authorisers for AAC devices. RESULTS: The findings suggest that there are several barriers (technical, social and political) influencing clinicians' decision to prescribe AAC devices. Technical challenges include the complexity of devices and viewing technology as a cure. Social barriers involve socio-demographic differences, readiness to use a device, social acceptance, attitudes, family's view of technology, and the priority of communication. Finally, several political barriers such as a shortage of speech pathologists, a complex prescription review process, inconsistent follow-up procedures, limitations of the consultative model, and gaps in funding and policy influenced clinicians' ability to prescribe AAC devices. Differences in philosophy of technology also influenced health providers' decision to prescribe AAC devices. CONCLUSIONS: Service providers and policy makers should be cognizant of the contextual factors influencing health provider's decision to prescribe AAC devices.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".