Atlantic Telehealth Knowledge Exchange
Bibliographic record
Abstract
Atlantic Canada has some of the earliest, most comprehensive, well-established networks, and innovative applications for telehealth in the country. The region offers a range of models for telehealth, in terms of management structure, coordination, funding, equipment, utilization, and telehealth applications. Collectively, this diversity, experience, and wealth of knowledge can significantly contribute to the development of a knowledge base for excellence in telehealth services. There is no formal process in place for the sharing of information amongst the provinces. Information sharing primarily occurs informally through professional contacts and participation in telehealth organizations. A core group of organizations partnered to develop a process for knowledge exchange to occur. This type of collaborative approach is favored in Atlantic Canada, given the region's economy and available resources. The Atlantic Telehealth Knowledge Exchange (ATKE) project centred on the development of a collaborative structure, information sharing and dissemination, development of a knowledge repository and sustainability. The project is viewed as a first step in assisting telehealth stakeholders with sharing knowledge about telehealth in Atlantic Canada. Significant progress has been made throughout the project in increasing the profile of telehealth in Atlantic Canada. The research process has captured and synthesized baseline information on telehealth, and fostered collaboration amongst telehealth providers who might otherwise have never come together. It has also brought critical awareness to the discussion tables of governments and key committees regarding the value of telehealth in sustaining our health system, and has motivated decision makers to take action to integrate telehealth into e-health discussions.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".