ECHO Care of the Elderly: Innovative Learning to Build Capacity in Long-term Care
Bibliographic record
Abstract
Background Older adults are entering long-term care (LTC) homes with more complex care needs than in previous decades, resulting in demands on point-of-care staff to provide additional and specialty services. This study evaluated whether Project ECHO® (Extension for Community Healthcare Outcomes) Care of the Elderly Long-Term Care (COE-LTC)—a case-based online education program—is an effective capacity-building program among interprofessional health-care teams caring for LTC residents. Methods A mixed-method, pre-and-post study comprised of satisfaction, knowledge, and self-efficacy surveys and exploration of experience via semi-structured interviews. Participants were interprofessional health-care providers from LTC homes across Ontario. Results From January–March 2019, 69 providers, nurses/nurse practitioners (42.0%), administrators (26.1%), physicians (24.6%), and allied health professionals (7.3%) participated in 10 weekly, 60-minute online sessions. Overall, weekly session and post-ECHO satisfaction were high across all domains. Both knowledge scores and self-efficacy ratings increased post-ECHO, 3.9% (p = .02) and 9.7 points (p < .001), respectively. Interview findings highlighted participants’ appreciation of access to specialists, recognition of educational needs specific to LTC, and reduction of professional isolation. Conclusion We demonstrated that ECHO COE-LTC can be a successful capacity-building educational model for interprofessional health-care providers in LTC, and may alleviate pressures on the health system in delivering care for residents.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".