Virtual Goals of Care Consultation for Advanced Frailty: a Qualitative Implementation Study Providing Insights from the Pandemic
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, long-term care (LTC) facilities faced challenges in establishing appropriate goals of care (GoC) for residents during health crises. To address this, a virtual specialist consultation program was implemented to align care interventions with residents' frailty and expected outcomes. Methods: We explored barriers and enablers to the implementation and sustainability of the program using structured interviews (n=20) with LTC leadership, health-care staff, and members of the program. Data were coded according to the constructs of the Consolidated Framework for Implementation Research (CFIR) using thematic analysis. Results: Participants described how the program improved care and reduced unnecessary transfers. Implementation was enabled by a high degree of tension for change, relative priority, relative advantage, and the team's shared mental model of frailty-care. Inconsistencies in GoC approaches and information silos between LTC and acute-care challenged implementation. Sustainability was hindered by decreased pandemic urgency, resulting in reallocation of resources to usual care. The need for a specialized GoC service in LTC became less obvious outside of a crisis. Conclusions: This implementation study provides important insights for future spread and scale of embedding virtual specialist consultation services into LTC. The findings underscore the importance of collegial relationships and shared care philosophies to effectively implement frailty-informed care initiatives during crises. However, sustaining cross-sectoral GoC services may be challenging amidst evolving workloads and prevailing cultural perceptions of end-of-life care needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".