Communications Technology and Motor Neuron Disease: An Australian Survey of People With Motor Neuron Disease
Bibliographic record
Abstract
BACKGROUND: People with Motor Neuron Disease (MND), of which amyotrophic lateral sclerosis (ALS) is the most common form in adults, typically experience difficulties with communication and disabilities associated with movement. Assistive technology is essential to facilitate everyday activities, promote social support and enhance quality of life. OBJECTIVE: This study aimed to explore the types of mainstream and commonly available communication technology used by people with MND including software and hardware, to identify the levels of confidence and skill that people with MND reported in using technology, to determine perceived barriers to the use of technology for communication, and to investigate the willingness of people with MND to adopt alternative modes of communication. METHODS: An on-line survey was distributed to members of the New South Wales Motor Neuron Disease Association (MND NSW). Descriptive techniques were used to summarize frequencies of responses and cross tabulate data. Free-text responses to survey items and verbal comments from participants who chose to undertake the survey by telephone were analyzed using thematic analysis. RESULTS: Responses from 79 MND NSW members indicated that 15-21% had difficulty with speaking, writing and/or using a keyboard. Commonly used devices were desktop computers, laptops, tablets and mobile phones. Most participants (84%) were connected to the Internet and used it for email (91%), to find out more about MND (59%), to follow the news (50%) or for on-line shopping (46%). A third of respondents used Skype or its equivalent, but few used this to interact with health professionals. CONCLUSIONS: People with MND need greater awareness of technology options to access the most appropriate solutions. The timing for people with MND to make decisions about technology is critical. Health professionals need skills and knowledge about the application of technology to be able to work with people with MND to select the best communication technology options as early as possible after diagnosis. If people with MND are willing to trial telehealth technology, there is potential for tele-consultations via Skype or its equivalent, with health professionals. People with MND can benefit from health professional involvement to match technology to their functional limitations and personal preferences. However, health professionals need a comprehensive understanding of the application of available technology to achieve this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".