Section 6: Education and Consumer Informatics: The Path towards eHealth: Obstacles along the Way
Bibliographic record
Abstract
Summary The two authors of this article share both a strong interest in, and deep concerns about, the use of eHealth (electronic information and communication technologies for improving or maintaining health). In this article, we identify some unanticipated obstacles to effective use of eHealth. We reflected upon the potential of information and communication technologies to transform the health system and its failure to achieve that potential. We chose seven obstacles: the insufficient emphasis on health in eHealth, the lack of time for reflection, the development of a fortress mentality, poor evaluation of efforts, lack of involvement of youth, inequity, and a parochial attitude that precludes economies of scale. Whenever possible, we provided examples of innovative initiatives that illustrate potential ways to meet our current challenges. We believe that the obstacles we describe in this article can be overcome. The impediments are not only technological, but also cognitive, financial and political. To succeed will require a major shift from our ethic of competition to one of generosity, commitment, and collaboration; enlightened, as opposed to narrow, self-interest.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".