Bibliographic record
Abstract
Ontario First Nations have demonstrated national leadership in the delivery of comprehensive on-reserve Telehealth/Telemedicine services1. For 25 remote and northern First Nations, KO Telemedicine (KOTM), the pioneer First Nations Telemedicine integrator in northwestern Ontario, has created a community-based videoconference and store-forward point-of-entry to clinicians, health educators, administrators and trainers. This service demonstrably improves on-reserve access to federal and provincial health services. KOTM provides an integrated approach to health human resource development and anticipates First Nation capacity to take advantage of improvements in the Canadian health system, such as the electronic health record. These issues are foregrounded in two sections. The first section surveys federal and provincial health policy and highlights Telemedicine capacities to meet longstanding First Nations access, ownership and resourcing objectives. The latter section describes the development of First Nations Telehealth/Telemedicine in Ontario and identifies service model requirements and options moving forward. Recommendations for collaborative development of a strategy for integrating First Nation Telehealth/Telemedicine with the Ontario Telemedicine Network are included at the end of this section.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.843 | 0.616 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".