Introduction to the Minitrack on Monitoring, Control, and Protection
Bibliographic record
Abstract
Little is known about e-health applications use by elderly in relation to social and system level determinants. We conducted a national survey of 2000 seniors in Canada assessing their use of technology and e-health applications, social determinants and interaction with the health care (HC) system. The findings demonstrate technological readiness (85% owned computers, 74% used Internet daily/weekly, 90% used e-mail), which does not translate into e-health applications use. Internet use to connect with a HC professional, access test results or patient portal, or medical appointment booking was very limited. The use of wearables, telemonitoring, and fall detection technology was also very low (11.9%, 9.4%, 4.2%, respectively). A digital divide exists among seniors that is underscored by significant associations between e-health applications use and social determinants. Private insurance and willingness to pay for quicker access are related to higher frequency of mApps and Internet use for accessing health information and exchanging with HC providers.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.035 |
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".