Bibliographic record
Abstract
Context awareness is an emerging field in pervasive computing with applications that have started to emerge in medical systems. The present work seeks to determine which contexts are important for medical applications and what various domains of context aware applications exist in healthcare. Methods: A systematic scoping review of context aware medical systems currently being used in healthcare settings was conducted. A search strategy was designed and applied to 8 databases, articles were then filtered based on their abstract, and then relevant articles had a screening questionnaire applied to their full texts prior to data extractions. Applications were grouped into context aware healthcare application domains based on past reviews and the results of the screening. Results: A total of 23 articles passed all screening levels and underwent data extraction. The most common contexts used were the user location (8/23 studies), demographic info (5/23 studies), movement status/activity level (6/23 studies), time of day (5/23 studies), phone usage patterns (5/23 studies), lab/vitals (7/23 studies), and patient history data (8/23 studies). Conclusions: The present work demonstrates that context aware healthcare applications are still in their infancy but have started to reach healthcare providers and patients. Significance: The present work has illuminated many of the early successful context aware healthcare applications. Additionally, the pivotal contexts leveraged by these systems have been discovered allowing future systems to focus on prioritizing the integration of these key contexts.
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 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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".