ECHO HIP: Phase 1: A needs assessment for continuing professional education for health information professionals
Bibliographic record
Abstract
Project Extension for Community Healthcare Outcomes (Project ECHO) is an innovative clinical education and tele-mentoring model that aims to democratize knowledge and build capacity in the healthcare workforce. ECHO uses a hub-and-spoke model to connect rural and underserved areas (spokes) to learn from each other and from inter-professional specialists (hub). Primarily focused on health care provider education, the ECHO model has not yet been applied to health librarianship. ECHO has the potential to be leveraged by health information professionals (HIP) to share best practices, develop specialty expertise and create a virtual community of practice. Each ECHO session is comprised of a didactic presentation, and case-based learning. To better understand the needs of HIPs and develop a curriculum, a needs assessment survey was developed and conducted. The survey focused on the need for a telehealth based model of continuing education (CE) for HIPs across Canada, and what topic areas should be considered. Based on preliminary data (n=46) 61% of respondents would likely attend ECHO sessions for HIPs, with an additional 35% uncertain. Preferred frequency of sessions is monthly. Didactic topics of greatest interest were literature searching, emerging technologies and evidence-based librarianship. All respondents, so far, have been from medium or large urban centres. There is definite interest in pursuing CE for HIPs based on the ECHO model. One limitation of the survey is the lack of respondents from small/rural communities, which may be addressed once data collection is complete. Future steps include determining funding models and infrastructure, and exploring multilingual options.
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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 | 0.062 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".