Positive impact of a telemedicine education program on practicing health care workers during the COVID-19 pandemic in Ontario, Canada: A mixed methods study of an Extension for Community Healthcare Outcomes (ECHO) program
Bibliographic record
Abstract
INTRODUCTION: In addition to shifting and expanding clinical responsibilities, rapidly evolving information and guidelines during the COVID-19 pandemic has made it difficult for health care workers (HCW) to synthesise and translate COVID-19 information into practice. This study evaluated whether a COVID-19-specific telemedicine education program (ECHO COVID) would impact health care workers' self-efficacy and satisfaction in the management of patients with COVID-19. METHODS: A prospective mixed methods parallel-design study was conducted among ECHO COVID participants using pre-post questionnaires and a focus group discussion. Questionnaire results were examined for changes in health care workers' self-efficacy and satisfaction. Focus group discussion data were analysed to explore health care workers' experience in ECHO COVID and the context of their practice during the COVID-19 pandemic. RESULTS: < 0.0001), independent of profession, years in practice, age group, or practice environment. Participants were highly satisfied with ECHO COVID sessions with a median score of 4 (IQR 4-5). Focus group discussion data indicated that health care workers gained knowledge through ECHO COVID and revealed facilitators for ECHO COVID program success, including the transition to virtual care, the practicability of knowledge provided, and a 'perspective from the trenches.' DISCUSSION: This study demonstrated that a telemedicine education program aimed to support health care workers in managing patients with COVID-19 had a positive impact on health care workers' self-efficacy and satisfaction. This impact was specifically mediated by the ECHO COVID program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".