A framework for telepsychiatric training and e-health: Competency-based education, evaluation and implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it