Telehealth to Support Practice in Remote Regions: A Survey Among Medical Residents
Bibliographic record
Abstract
Medical workforce distribution represents a particular challenge in Quebec, Canada. Telehealth is considered a promising tool to provide access to health care, but also to increase professional support in remote regions. This survey was aimed at exploring medical residents' perceptions toward telehealth and its potential impact on their intention to practice in remote regions. A questionnaire was distributed to a convenient sample of medical residents. A total of 67 questionnaires were analyzed. Analyses were performed to explore relationships between factors related to residents' intention to practice in remote regions and factors associated with their intention to use telehealth. Residents perceived telehealth positively and thought that it could influence favorably medical practice in remote regions. Furthermore, telehealth was associated with better continuing medical education and this factor is a strong predictor of medical residents' intention to practice in remote regions. Telehealth constitutes a precious tool to support the delivery of health services in remote regions and to facilitate the work of physicians. Nonetheless, telehealth alone would not solve medical resource shortages in remote regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".