BARRIERS AND FACILITATORS FOR ECONSULT: IMPROVING ACCESS TO SPECIALIST ADVICE IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Long-term care (LTC) residents continue to receive most of their specialist care outside of the home. These visits present significant risks and burdens for residents. Such risks can be mitigated by eConsult, which facilitates timely, resident-specific, and practical recommendations between primary care providers (PCPs) and specialists to support clinical decision making, reduce unnecessary transfers, and positively impact resident care and quality of life. During focus groups in LTC homes where eConsult is used, PCPs, senior leadership, and a nurse champion recognized eConsult as having great value for residents, especially those with challenges travelling outside of the home. However, they also described LTC homes as under resourced, with minimal IT infrastructure and coupled with time constraints present barriers to eConsult adoption in LTC. Enabling factors (engaging clinician champions, establishing delegates, integration into existing workflows) similarly identified as supporting eConsult implementation more broadly were discussed.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".