Opening the digital front door for individuals using long-term in-home ventilation (LIVE) during a pandemic- implementation, feasibility and acceptability
Bibliographic record
Abstract
Background The COVID-19 pandemic led to an unprecedented need for virtual healthcare that was safe, acceptable and feasible to deliver. In May 2020, we launched the Long-term In-Home Ventilator Engagement (LIVE) program for ventilator assisted individuals using ventilators hosted on an e-platform in Ontario, Canada. Objectives To assess the acceptability, appropriateness, feasibility and usability of the LIVE program reported by patients, family caregivers, and healthcare providers (HCP). Design and Methods We conducted a cross-sectional study. We provided HCPs participating in the LIVE program anonymized questionnaires (Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM), and mHealth App Usability (MAUQ). Patients and family caregivers completed the AIM and MAUQ. Questionnaires were administered via an e-platform. Results We recruited 105/251 (42%) patients and family caregivers and 42/48 (87.5%) HCPs. Patients and caregivers rated a mean (SD) overall AIM score of 4.3 (0.7) (maximum score 5; higher scores indicate greater acceptability) and a mean (SD) overall MAUQ score of 5.8 (1.5) (maximum score 7; higher scores indicate greater useability). HCPs rated a mean (SD) overall AIM score of 4.3 (0.7), IAM score of 4.3 (0.8), FIM score of 4.2 (0.7) and overall MAUQ score of 5.6 ± 1.5. There were no differences in AIM ((4.3 (0.7) vs 4.3 (0.8), p = 1) or MAUQ (5.8 (1.5) vs 5.6 (1.5), p = 0.5) scores between patients/ family caregivers and HCPs. Interpretation This study suggests that the LIVE program was acceptable, appropriate, feasible, and usable from the perspective of patients, family caregivers and HCPs.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".