Evaluating Implementation Factors of Indigenous Communities in Northern Ontario for eConsult
Bibliographic record
Abstract
The creation of innovative platforms is of limited benefit if not implemented properly with careful consideration of regional contexts. Digital health platforms can be a tool that may improve access to quality care for residents of Northern Ontario. The health innovations framework of Chaudoir et al. [1] was used to address patient, provider, organization, and system level factors relevant to the implementation of electronic consultation (eConsult) in the North West Local Health Integration Network (LHIN) for Indigenous communities. An environmental scan was conducted through a systematic literature search of three databases and grey literature. For the implementation of eConsult in Indigenous communities in Northern Ontario, it was recommended that: (1) an Indigenous care expert should be consulted to include features that ensure the provision of culturally competent care to patients; (2) further investigation into the role of nurses and nurse practitioners in Indigenous communities should be conducted; (3) the possibility of partnering with provincial Aboriginal Health Access Centres and the Northern Ontario School of Medicine should be explored; (4) the gain of federal government funding and support; and (5) the function of eConsult should potentially extend to act as a centralized source of public health information. Extreme regional diversity is prevalent across Northern Ontario, and additional analyses should be done at a more local level prior to the implementation of eConsult.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".