Implementation study of virtual goals of care consultation for advanced frailty: Lessons learned from the Pandemic
Bibliographic record
Abstract
Abstract Background Long-term care (LTC) residents disproportionately experienced critical illness and death in the setting of COVID-19. In response, a virtual specialist consultation service known as the "Med-LTC program" was developed to support residents and their families with advance care planning to establish acceptable levels of health care intervention. The program significantly improved goals of care to align with resident frailty and expected outcomes in the event of an acute health crisis. A post-implementation inquiry explored the factors that influenced the program success to inform ongoing collaboration between acute care and LTC sectors in the delivery of frailty-informed care. Methods The factors that impacted program effectiveness and implementation outcomes were systematically explored using the Consolidated Framework for Implementation Research (CFIR). Structured interviews (n = 20) were conducted with LTC leadership, LTC healthcare staff, and members of the program. Qualitative data were extracted from the transcripts and coded to the domains and constructs of the CFIR. Results The CFIR constructs of inner setting, intervention characteristics, and individual characteristics were most strongly represented, with emphasis on tension for change, relative priority, relative advantage, and other personal attributes (shared team vision grounded in pre-existing social bonds) featuring prominently. Challenges in terms of complexity, cost, and relative priority all contributed to the ultimate lack of sustainability of the virtual program in the post-acute phase of the COVID-19 pandemic. Conclusions Application of the CFIR identified key barriers to and enablers of program success and providing important insights into factors supporting the development of more sustainable future iterations of virtual specialist consultation to establish goals of care in LTC.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| 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".