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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".