Acceptability of the Long-Term In-Home Ventilator Engagement virtual intervention for home mechanical ventilation patients during the COVID-19 pandemic: A qualitative evaluation
Bibliographic record
Abstract
BackgroundClinical management of ventilator-assisted individuals (VAIs) was challenged by social distancing rules during the COVID-19 pandemic. In May 2020, the Long-Term In-Home Ventilator Engagement (LIVE) Program was launched in Ontario, Canada to provide intensive digital care case management to VAIs. The purpose of this qualitative study was to explore the acceptability of the LIVE Program hosted via a digital platform during the COVID-19 pandemic from diverse perspectives.MethodsWe conducted a qualitative descriptive study (May 2020–April 2021) comprising semi-structured interviews with participants from eight home ventilation specialty centers in Ontario, Canada. We purposively recruited patients, family caregivers, and providers enrolled in LIVE. Content analysis and the theoretical concepts of acceptability, feasibility, and appropriateness were used to interpret findings.ResultsA total of 40 individuals (2 VAIs, 18 family caregivers, 20 healthcare providers) participated. Participants described LIVE as acceptable as it addressed a longstanding imperative to improve care access, ease of use, and training provided; feasible for triaging problems and sharing information; and appropriate for timeliness of provider responses, workflows, and perceived value. Negative perceptions of acceptability among healthcare providers concerned digital workload and fit with existing clinical workflows. Perceived benefits accorded to LIVE included enhanced physical and psychological safety in the home, patient–provider relations, and VAI engagement in their own care.ConclusionsStudy findings identify factors influencing the LIVE Program's acceptability by patients, family caregivers, and healthcare providers during pandemic conditions including enhanced access to care, ease of case management triage, and VAI safety. Findings may inform the implementation of digital health services to VAIs in non-pandemic circumstances.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".