Surgical, trauma and telehealth capacity in Indigenous communities in Northern Quebec: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Delivering trauma and surgical care to Northern Quebec presents unique challenges owing to the region's remoteness, extreme weather and limited transport; the expansion of telehealth could help address these difficulties. We aimed to evaluate current surgical, trauma and telemedicine capacity in Nunavik, Quebec. METHODS: We used validated assessment tools, including the Personnel, Infrastructure, Procedures, Equipment and Supplies survey, the International Assessment of Capacity for Trauma index and the Maryland Health Care Commission Telemedicine Readiness tool to evaluate surgical, trauma and telemedicine capacity, respectively. We adapted these tools to the Northern Quebec context through discussions with local leadership. Data were collected in 2 regional hospitals - the Ungava Tulattavik Health Centre (UTHC) and the Inuulitsivik Health Centre (IHC) - and 12 Centres locaux de services communautaires (CLSCs; local community services centres) in 6 villages along the Hudson Bay coast and 6 villages along the Ungava Bay coast through iterative discussions with 4 chief nurses from each regional hospital and set of CLSCs; resources were confirmed through on-site evaluation by the respondents. We performed a descriptive analysis of the data. RESULTS: Surgical capacity was highest in the IHC (6.76) and lowest in the Ungava Bay CLSCs (5.52). Personnel (0%-0%) and procedures (13%-33%) were the least available resources. Trauma capacity was highest in the IHC (7.25) and lowest in the Hudson Bay CLSCs (5.58). Although equipment (90%-100%) and supplies (100%-100%) were readily available, personnel (0%-0%) and procedures (25%-56%) were lacking. The UTHC was most prepared for telehealth (67.80%), and the Ungava Bay CLSCs achieved a lower score (51.13%). Underdeveloped telehealth criteria included funding, administrative support, quality improvement and physical spaces (all 33%-67%). CONCLUSION: Acute care capacity in Nunavik appears heterogeneous, with readily available equipment and supplies, but a lack of personnel capable of performing lifesaving procedures. To address the need for telemedicine, future initiatives should focus on improving funding, administrative support, physical spaces and quality-improvement initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".