A Framework for Competencies for the Use of Mobile Technologies in Psychiatry and Medicine: Scoping Review
Bibliographic record
Abstract
BACKGROUND: To ensure quality care, clinicians need skills, knowledge, and attitudes related to technology that can be measured. OBJECTIVE: This paper sought out competencies for mobile technologies and/or an approach to define them. METHODS: A scoping review was conducted to answer the following research question, "What skills are needed for clinicians and trainees to provide quality care via mHealth, have they been published, and how can they be made measurable and reproducible to teach and assess them?" The review was conducted in accordance with the 6-stage scoping review process starting with a keyword search in PubMed/Medical Literature Analysis and Retrieval System Online, APA PsycNET, Cochrane, EMBASE, PsycINFO, Web of Science, and Scopus. The literature search focused on keywords in 4 concept areas: (1) competencies, (2) mobile technologies, (3) telemedicine mode, and (4) health. Moreover, 2 authors independently, in parallel, screened the search results for potentially relevant studies based on titles and abstracts. The authors reviewed the full-text articles for final inclusion based on inclusion/exclusion criteria. Inclusion criteria were keywords used from concept area 1 (competencies) and 2 (mobile technologies) and either 3 (telemedicine mode) or 4 (health). Exclusion criteria included, but were not limited to, keywords used from a concept area in isolation, discussion of skills abstractly, outline or listing of what clinicians need without detail, and listing immeasurable behaviors. RESULTS: From a total of 1232 results, the authors found 78 papers eligible for a full-text review and found 14 papers directly relevant to the 4 key concepts. Although few studies specifically discussed skills, the majority were clinical studies, and the literature included no lists of measurable behaviors or competency sets for mobile technology. Therefore, a framework for mobile technology competencies was built according to the review, expert consensus, and recommendations of the Institute of Medicine's Health Professions Education Summit and Accreditation Council of Graduate Medical Education framework. This framework borrows from existing competency framework domains in telepsychiatry and social media (patient care, medical knowledge, practice-based learning and improvement, systems-based practice, professionalism, and interpersonal skills and communication) and added domains of mHealth clinical decision support, device/technology assessment/selection, and information flow management across an electronic health record platform. mHealth Asynchronous components require additional traditional learning, teaching, supervisory and evaluation practices. Interactive curricula with case-, problem-, and system-based teaching may help faculty focus on decision making and shape skills and attitudes to complement clinical exposure. CONCLUSIONS: Research is needed on how to customize implementation and evaluation of mHealth competencies and to ensure skill development is linked to the quality of care. This will require the management of organizational change with technology and the creation of a positive electronic culture in a complex policy and regulatory environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".