The Telehealth Skills, Training, and Implementation Project: An Evaluation Protocol
Bibliographic record
Abstract
BACKGROUND: Telehealth appears to be an ideal mechanism for assisting rural patients and doctors and medical students/registrars in accessing specialist services. Telehealth is the use of enhanced broadband technology to provide telemedicine and education over distance. It provides accessible support to rural primary care providers and medical educators. A telehealth consultation is where a patient at a general practice, with the assistance of the general practitioner or practice nurse, undertakes a consultation by videoconference with a specialist located elsewhere. Multiple benefits of telehealth consulting have been reported, particularly those relevant to rural patients and health care providers. However there is a paucity of research on the benefits of telehealth to medical education and learning. OBJECTIVE: This protocol explains in depth the process that will be undertaken by a collaborative group of universities and training providers in this unique project. METHODS: Training sessions in telehealth consulting will be provided for participating practices and students. The trial will then use telehealth consulting as a real-patient learning experience for students, general practitioner trainees, general practitioner preceptors, and trainees. RESULTS: Results will be available when the trial has been completed in 2015. CONCLUSIONS: The protocol has been written to reflect the overarching premise that, by building virtual communities of practice with users of telehealth in medical education, a more sustainable and rigorous model can be developed. The Telehealth Skills Training and Implementation Project will implement and evaluate a theoretically driven model of Internet-facilitated medical education for vertically integrated, community-based learning environments.
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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 | 0.170 | 0.099 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.014 |
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".