An Industry, Clinical, and Academic Telehealth Partnership Venture: Progress, Goals Achieved, and Lessons Learned
Bibliographic record
Abstract
The goal of this project was to develop and test a multi-application telehealth workstation with an interface seamless to the end-users. This project required collaboration among the private sector, a clinical regional referral setting, a clinical tertiary receiving center, and a university academic unit. The project applied the usability testing methodology to design and test the workstation: (1) the developmental phase focused on planning, prototype workstation development, and end-user trials in the private sector research and development environment, and (2) the operational testing phase moved the workstation into the clinical setting. The latter included training of end-users and addressing policy and ethical issues. In addition, the partners documented the goals achieved and lessons learned. The project resulted in the refinement of the workstation for clinical applications. The unique, diverse, and at times complementary goals and lessons learned by each partner are noted. Collaboration around a shared goal was a key element in achieving the refined workstation. Collaborators reported that the lessons learned will aid them as they pursue telehealth activities.
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 | 0.081 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".