The Origin and Impact of Ideals in eHealth Research: Experiences From the U-CARE Research Environment
Bibliographic record
Abstract
BACKGROUND: The prevalence of information technology (IT) in society is a foundation for new modes of interaction between patients and health specialists. IT plays an important role in the renewal of care. Several countries have incorporated eHealth plans into their national health strategies. Part of the eHealth evolution concerns Internet psychological treatment and psychosocial care. These interventions are complex to design and evaluate due to legal, ethical, organizational, technical, and methodological challenges. OBJECTIVE: The objective of our study was to seek to make explicit contributions to the understanding of ideals in eHealth research, and illuminate their implications for establishing an effective research environment. Our analysis draws from three years of experience in establishing an eHealth research environment, and the literature. METHODS: We worked inductively to characterize challenging research ideals, and their origins, in our environment. Thereafter, we made a selective search of the literature to scrutinize and illuminate each ideal and it's implications. RESULTS: In this work, we propose a structured approach to address ideals in eHealth research. The scrutinized ideals are accountability, innovation, rigor, relevance, and sustainability. The approach supports researchers to systematically understand the ideals, their origin, and to manage their implications within an eHealth research environment. CONCLUSIONS: The complexity of eHealth research causes a need for sustainable, multi-disciplinary research environments. There is a need for a structured approach to organize eHealth research. The proposed approach helps to systematically scrutinize ideals, thus promoting high quality research.
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 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 | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".