Postgraduate trainee views on eHealth at a distributed medical campus.
Bibliographic record
Abstract
Purpose: e-Health is a rapidly evolving field that cuts across specialties however; there is a gap in development and evaluation of training for postgraduates in residency programs. This is a multicentre, collaborative effort among faculty from the departments of Psychiatry, Geriatrics and Internal Medicine in partnership with Ontario Telehealth Network to assess the needs of postgraduate residents in ehealth and build a pilot program to address identified learning gaps. Methodology: We conducted a needs assessment (Appendix A) through an online survey to investigate the self-perceived knowledge, gaps and barriers to eHealth of medical resident physicians at the McMaster University DeGroote School of Medicine Waterloo Regional Campus (WRC), Kitchener, Ontario, Canada Results: All respondents identified that they would be interested in education in telehealth and all of them felt that they would have to use telehealth in their future practices. However, 83.3% did not feel confident using telemedicine in clinical practice. Based on the results of the needs assessment, we have built a pilot rotation in which postgraduate trainees can practice telehealth skills in an interdisciplinary setting.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".