Telehealth’s Potential in Rural Communities for Improving Specialist Care: Access to an Autism Diagnosis for Children in Alberta
Bibliographic record
Abstract
The COVID-19 pandemic accelerated the growth of telehealth (Madigan et al. 2021, 1; Virtual Care Task Force 2022, 1; Canadian Institute for Health Information 2023, 4). Virtual care went from 10 to 20 per cent in 2019 to 40 per cent in 2021 (Virtual Care Task Force 2022, 4). More recently, the 2023 Canadian Digital Health Survey found that 46 per cent of Canadians have had a telephone visit and 21 per cent have utilized video conferencing for a health care visit (Canada Health Infoway 2024, 12). The 2023 Canadian Digital Health Survey suggests that there is unmet demand (Canada Health Infoway 2024, 13).1 The report found that the unmet demand was 38 per cent for video visits and 27 per cent for telephone consultations. Unmet demand, combined the “the growth in Canadian physicians’ adoption of information technology” shows promising telehealth growth for Canada (Canadian Institute for Health Information 2023, 4).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".