Home Mechanical Ventilation in Canada: A National Survey
Bibliographic record
Abstract
BACKGROUND: No comprehensive Canadian national data describe the prevalence of and service provision for ventilator-assisted individuals living at home, data critical to health-care system planning for appropriate resourcing. Our objective was to generate national data profiling service providers, users, types of services, criteria for initiation and monitoring, ventilator servicing arrangements, education, and barriers to home transition. METHODS: Eligible providers delivering services to ventilator-assisted individuals (adult and pediatric) living at home were identified by our national provider inventory and referrals from other providers. The survey was administered via a web link from August 2012 to April 2013. RESULTS: The survey response rate was 152/171 (89%). We identified 4,334 ventilator-assisted individuals: an estimated prevalence of 12.9/100,000 population, with 73% receiving noninvasive ventilation (NIV) and 18% receiving intermittent mandatory ventilation (9% not reported). Services were delivered by 39 institutional providers and 113 community providers. We identified variation in initiation criteria for NIV, with polysomnography demonstrating nocturnal hypoventilation (57%), daytime hypercapnia (38%), and nocturnal hypercapnia (32%) as the most common criteria. Various models of ventilator servicing were reported. Most providers (64%) stated that caregiver competency was a prerequisite for home discharge; however, repeated competency assessment and retraining were offered by only 45%. Important barriers to home transition were: insufficient funding for paid caregivers, equipment, and supplies; a shortage of paid caregivers; and negotiating public funding arrangements. CONCLUSIONS: Ventilatory support in the community appears well-established, with most individuals managed with NIV. Although caregiver competency is a prerequisite to discharge, ongoing assessment and retraining were infrequent. Funding and caregiver availability were important barriers to home transition.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| 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".