The empowerment and quality health value propositions of e-health
Bibliographic record
Abstract
E-health, as well as its value and benefits, has been characterized as a concept defined in various ways depending on intended audience and use. Attempts to define, characterize and appreciate e-health inadvertently portray it as something out of main stream academia; thus, undermining the relevance and importance of the transformation capabilities of e-health on the practice of health care from the individual and organizational perspectives. In order to contribute towards an understanding and appreciation of e-health as a main stream concept, we propose the use of existing models, theories and principles in support of e-health. Specifically, the empowerment theory and the principles of quality health will be used to discuss the value proposition of e-health. An understanding of the e-health value proposition is important, because it helps organizations to develop a shared vision and context, which in turn keeps organizations focused and realistic as they expend resources and adopt e-health. It also helps e-health consumers understand what is possible and impossible, and how they can best participate in e-health for the betterment of their health and health care.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".