Evaluating the Use of Smart Home Technology by People With Brain Impairment: Protocol for a Single-Case Experimental Design
Bibliographic record
Abstract
BACKGROUND: Smart home technologies are emerging as a useful component of support delivery for people with brain impairment. To promote their successful uptake and sustained use, focus on technology support services, including training, is required. OBJECTIVE: The objective of this paper is to present a systematic smart home technology training approach for people with brain impairment. In addition, the paper outlines a multiple-baseline, single-case experimental design methodology to evaluate training effectiveness. METHODS: Adult participants experiencing acquired brain impairment who can provide consent to participate and who live in housing where smart home technology is available will be recruited. Target behaviors will be identified in consultation with each participant based on his or her personal goals for technology use. Target behaviors may include participant knowledge of the number and type of technology functions available, frequency of smart home technology use, and number of function types used. Usage data will be gathered via log-on smart home technology servers. A smart technology digital training package will also be developed and left on a nominated device (smartphone, tablet) with each participant to use during the trial and posttrial, as desired. Measures of the target behavior will be taken throughout the baseline, intervention, and postintervention phases to provide the evidence of impact of the training on the target behaviors and ascertain whether utilization rates are sustained over time. In addition, trial results will be analyzed using structured visual analysis, supplemented with statistical analysis appropriate to single-case methodology. RESULTS: While ascertaining the effectiveness of this training protocol, study results will offer new insights into technology-related training approaches for people with brain impairment. Preliminary data collection has been commenced at one supported housing site, with further scoping work continuing to recruit participants from additional sites. CONCLUSIONS: Evaluation evidence will assist in planning for the smart technology set-up as well as training and support services necessary to accompany the provision of new devices and systems. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/10451.
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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 | 0.073 | 0.050 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".