Exploring learners’ perspectives of a Project ECHO palliative care education curriculum in resource-limited settings in light of key learning theories
Bibliographic record
Abstract
Abstract Background: Project ECHO (Extension of Community Healthcare Outcomes) is a well-established telehealth educational platform developed to improve access to specialist care training in underserved areas. The objective of this study is to explore the learning experiences of participants in a Project ECHO targeting care providers in India and explore considerations for how ECHO programs could be modified to cater to the unique learning needs of individuals in low- and middle-income countries. Methods : We implemented a one-year Project ECHO on pediatric palliative care (ECHO PPC) targeting healthcare providers in India. The program consisted of 24 bi-weekly sessions (March 2018-February 2019) hosted by the Hyderabad Centre for Palliative Care in Hyderabad, India. Learners who attended at least 20% (5 sessions) were eligible to participate. Data related to demographic characteristics of ECHO PPC participants were collected. Four focus groups were conducted (February-April 2019) either online via videoconference or in person. Focus groups recordings were transcribed, coded and independently verified. The codes were then arranged into overarching themes.Results : Thirty-six learners were eligible and invited to participate in the study. Seventeen individuals completed the study. Following coding and analysis, two major themes and seven sub-themes were identified. The themes included: (1) Benefits of participation in ECHO PPC (creation of a community of practice; opportunity to exchange cultures, ideas and experiences; supportive role of the facilitator and peers; increased relevant knowledge and skills; and access to additional learning resources) and (2) Barriers to participation (difficulties with time and practice schedule management and language and sociocultural factors). Conclusion: The key principals of learning theories were incorporated into forming the Project ECHO conceptual model in this study. PPC Project ECHO facilitated community building, stimulated professional interest and additional learning outside of the program. However, addressing the barriers and challenges might likely enhance the success of the program in future.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".