Defining competencies for the practice of telepsychiatry through an assessment of resident learning needs
Bibliographic record
Abstract
BACKGROUND: A foundational assessment of learning needs is missing from previous reports of telepsychiatry curricula. We used an in-depth needs assessment to identify specific skills required for the practice of effective telepsychiatry, and provide an evidence base to guide the development of telepsychiatry curricula in postgraduate psychiatry training. Many of these skills set telepsychiatry apart from practice in traditional face-to-face clinical settings, or result from adaptations to clinical practice to meet the needs of a telepsychiatry interface in patient care. METHODS: We used a qualitative, modified grounded theory approach to gain insight into areas of importance for telepsychiatry training in postgraduate psychiatry residency. 16 interviews of faculty and residents (9 and 7 interviews, respectively), allowed participants to reflect on their experiences in telepsychiatry. Data were then thematically analyzed. RESULTS: Interview respondents identified important aspects of the context for telepsychiatry training; the skills required to competently practice telepsychiatry; and the desired teaching and learning methods for acquiring these skills. Specific domains of competency were identified: technical skills; assessment skills; relational skills and communication; collaborative and interprofessional skills; administrative skills; medico-legal skills; community psychiatry and community-specific knowledge; cultural psychiatry skills, including knowledge of Indigenous cultures; and, knowledge of health systems. The skills identified in this study map well to competency- based medical education frameworks. CONCLUSIONS: Telepsychiatry is increasingly being adopted as a solution to health systems problems such as regional disparities in access to care, and it requires explicit competency development. Ensuring adequate and quality exposure to telepsychiatry during residency training could positively impact our health systems and health equity.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".