Effective Use of Virtual Communities for Health-Purposes in the Elderly
Bibliographic record
Abstract
New strategies must be developed to adequately answer the growing health needs of the elderly. Virtual communities targeted to older adults may represent interesting tools, ranging from providing health-related information to operating preventive programs, or simply reducing social isolation and thus increasing the quality of life of the elderly. Besides generic issues shared with any eHealth virtual community (e.g., user engagement, participation, acceptance), the use of virtual communities by the elderly also bears some specific challenges, including limitations related to access to care or the presence of individuals with (age-related) disability. This Chapter describes some of the factors which should be taken into consideration when designing eHealth strategies aiming at developing or supporting virtual communities targeted to elderly population, and emphases the importance of integrating health-oriented senior-targeted virtual communities into holistic approach to allow for the communities to optimally develop and consolidate, and reach its goal in terms of health benefits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".