Overview of Policies, Guidelines, and Standards for Active Assisted Living Data Exchange: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: A primary concern for governments and health care systems is the rapid growth of the aging population. To provide a better quality of life for the elderly, researchers have explored the use of wearables, sensors, actuators, and mobile health technologies. The term AAL can be referred to as active assisted living or ambient assisted living, with both sometimes used interchangeably. AAL technologies describes systems designed to improve the quality of life, aid in independence, and create healthier lifestyles for those who need assistance at any stage of their lives. OBJECTIVE: The aim of this study was to understand the standards and policy guidelines that companies use in the creation of AAL technologies and to highlight the gap between available technologies, standards, and policies and what should be available for use. METHODS: A literature review was conducted to identify critical standards and frameworks related to AAL. Interviews with 15 different stakeholders across Canada were carried out to complement this review. The results from interviews were coded using a thematic analysis and then presented in two workshops about standards, policies, and governance to identify future steps and opportunities regarding AAL. RESULTS: Our study showed that the base technology, standards, and policies necessary for the creation of AAL technology are not the primary problem causing disparity between existing and accessible technologies; instead nontechnical issues and integration between existing technologies present the most significant issue. A total of five themes have been identified for further analysis: (1) end user and purpose; (2) accessibility; (3) interoperability; (4) data sharing; and (5) privacy and security. CONCLUSIONS: Interoperability is currently the biggest challenge for the future of data sharing related to AAL technology. Additionally, the majority of stakeholders consider privacy and security to be the main concerns related to data sharing in the AAL scope. Further research is necessary to explore each identified gap in detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".