Exploring factors affecting the timely transition of ventilator assisted individuals in Ontario from acute to long-term care: Perspectives of healthcare professionals
Bibliographic record
Abstract
Rationale: Ventilator Assisted Individuals (VAIs) frequently remain in intensive care units (ICUs) for a prolonged period once clinically stable due to a lack of transition options. These VAIs occupy ICU beds and resources that patients with more acute needs could better utilize. Moreover, VAIs experience improved outcomes and quality of life in long-term and community-based environments. Objective: To better understand the perspectives of healthcare providers (HCPs) working in an Ontario ICU regarding barriers and facilitators to referral and transition of VAIs from the ICU to a long-term setting. Methods: We conducted semi-structured interviews with ten healthcare providers involved in VAI transitions. Main Results: Perceived barriers included long wait times for long-term care settings, insufficient bed availability at discharge locations, medical complexity of patients, long waitlists, and a lack of transparency of waitlists. Facilitators included strong partnerships and trusting relationships between referring and discharge locations, a centralized referral system, and utilization of community partnerships across care sectors. Conclusions: Insufficient resourcing of long-term care is a key barrier to transitioning VAIs from ICU to long-term settings; strong partnerships across care sectors are a facilitator. System-level approaches, such as a single-streamlined referral system, are needed to address key barriers to timely transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".