Best Practice in Prolonged Mechanical Ventilation: A Qualitative Study of Healthcare Provider Perspectives
Bibliographic record
Abstract
Background & Purpose: Patients who require Prolonged Mechanical Ventilation (PMV) are a relatively small but complex and vulnerable subset of patients treated in the intensive care unit (ICU). Significant heterogeneity in practice patterns exists and best practice is largely unknown. The goal of this study is to engage healthcare providers (HCPs) to identify and describe best care practices for patients requiring PMV. Methods: A qualitative descriptive method was used. Using purposeful sampling, we recruited medical doctors (MD), nurse practitioners (NP), registered nurses (RN) and respiratory therapists (RT) from hospitals across Alberta to participate in virtual, semi-structured interviews. Interviews were recorded, transcribed verbatim and analyzed concurrently using the principles of thematic analysis. Results: We identified 5 best practice themes: 1) patient and family engagement 2) team dynamics: collaboration and autonomy 3) developing a structured plan and process 4) ICU physical environment 5) discharge and disposition. Overall, these themes represent a collaborative approach to PMV that includes structured planning and comprehensive care. Conclusion: Patients requiring PMV are a complex clinical population with unique needs. The themes identified can be adopted in existing ICU environments and can guide the expansion of high-quality PMV programs. Key Words: Prolonged Mechanical Ventilation; Healthcare Providers; Qualitative Description Critical Care Medicine; Patient-Centered Care; Intensive Care Unit.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".