Telemedicine and surgical coordination for Indigenous children from remote communities in northern Quebec
Bibliographic record
Abstract
BACKGROUND: Pediatric patients from remote Indigenous communities in northern Quebec face substantial challenges accessing surgical care, often requiring lengthy air travel to urban hospitals. We sought to quantify time spent away from home for surgical care and explore telemedicine use during the perioperative period for this population. METHODS: We conducted a retrospective chart review of children from Nunavik and Terres-Cries-de-la-Baie-James who received surgical care at the Montreal Children's Hospital between 2011 and 2021. Dates of preoperative consultation, surgery, and postoperative follow-up were recorded, along with encounter modality. RESULTS: Of 914 patients identified, 40.9% required urgent surgery. For elective procedures, 59.1% of patients waited 14 days or longer for surgery after initial consultation. Postoperatively, 46.8% had follow-up appointments within 7 days of discharge, while 26.1% waited more than 14 days. Telemedicine was used in only 2.2% of elective consultations and 5.5% of follow-up appointments. CONCLUSION: Wait times for surgery and initial follow-up appointments often exceeded 2 weeks and required return trips to Montréal, while telemedicine adoption remained limited across departments. Efforts to reduce wait times, increase telemedicine adoption, and enhance culturally safe practices could improve access and care experiences for patients from northern Quebec.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".