A framework for telepsychiatric training and e-health: Competency-based education, evaluation and implications
Bibliographic record
Abstract
Telepsychiatry (TP; video; synchronous) is effective, well received and a standard way to practice. Best practices in TP education, but not its desired outcomes, have been published. This paper proposes competencies for trainees and clinicians, with TP situated within the broader landscape of e-mental health (e-MH) care. TP competencies are organized using the US Accreditation Council of Graduate Medical Education framework, with input from the CanMEDS framework. Teaching and assessment methods are aligned with target competencies, learning contexts, and evaluation options. Case examples help to apply concepts to clinical and institutional contexts. Competencies can be identified, measured and evaluated. Novice or advanced beginner, competent/proficient, and expert levels were outlined. Andragogical (i.e. pedagogical) methods are used in clinical care, seminar, and other educational contexts. Cross-sectional and longitudinal evaluation using quantitative and qualitative measures promotes skills development via iterative feedback from patients, trainees, and faculty staff. TP and e-MH care significantly overlap, such that institutional leaders may use a common approach for change management and an e-platform to prioritize resources. TP training and assessment methods need to be implemented and evaluated. Institutional approaches to patient care, education, faculty development, and funding also need to be studied.
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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 | 0.079 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".