Home Automation for Adults With Disability Following an Injury: Protocol for a Social Return on Investment Study
Bibliographic record
Abstract
BACKGROUND: People with disability following a serious injury require long-term care. The most common injuries resulting in long-term disability are spinal cord and acquired brain injuries. While the long-term effects are difficult to predict and will vary between individuals, the costs of care and recovery span well beyond the initial treatment phase and include long-term care. Long-term care is changing with the availability and advances in cost and function of technologies, such as home automation. "Home automation" refers to technology that automates or remotely controls household functions. Home automation costs vastly differ, but home automation has the potential to positively impact the lives of people with disabilities. However, there is a dearth of evidence relating to the impact of home automation for people with a disability and few rigorous evaluations about the costs and return on investment. OBJECTIVE: The purpose of this study is to describe the impact of home automation for people with long-term disability following a serious injury (such as a motor vehicle accident) using case studies, and by conducting an evaluation of the costs and outcomes for individuals, families, and the wider community using a Social Return on Investment (SROI) approach. METHODS: SROI is a form of economic evaluation that develops a theory of change to examine the relationship among inputs, outputs, and outcomes and, in recent years, has gained popularity internationally, including in Australia. SROI has six phases: (1) identify scope and stakeholders, (2) map outcomes, (3) evidence outcomes and give them value, (4) establish impact, (5) calculate the SROI, and (6) report findings. Individuals with a disability who use home automation and key stakeholders will be interviewed. Stakeholders will be individuals involved in home automation for people with disabilities, such as allied health professionals, medical practitioners, equipment suppliers, engineers, and maintenance professionals. Users of home automation will be people who have a disability following a serious injury, have the capacity to provide consent, and have 1 or more elements of home automation. The impact of home automation will be established with financial proxies and appropriate discounts applied to avoid overestimating the social return. The SROI ratio will be calculated, and findings will be reported. RESULTS: The project was funded in November 2021 by the Lifetime Support Authority. Recruitment is underway, and data collection is expected to be completed by October 2022. The final results of the study will be published in March 2023. CONCLUSIONS: To our knowledge, this study represents the first study in Australia and internationally to employ SROI to estimate the social, personal, and community outcomes of home automation for people with a disability following a serious injury. This research will provide valuable information for funders, consumers, researchers, and the public to guide and inform future decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42493.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".