Characteristics, Barriers, and Facilitators of Virtual Decision-Making Capacity Assessments During the COVID-19 Pandemic: Online Survey
Bibliographic record
Abstract
BACKGROUND: With a growing older adult population, the number of persons with dementia is expected to rise. Consequently, the number of persons needing decision-making capacity assessments (DMCA) will increase. The COVID-19 pandemic has impacted how we deliver patient care including DMCAs with a much more rapid shift to virtual assessments. Virtual DMCAs offer patients and health care professionals distinct advantages over in-person delivery by improving reach, access, and timely provision of health care. However, questions have arisen as to whether DMCAs can be effectively conducted virtually. OBJECTIVE: This study aimed to determine the characteristics, barriers, and facilitators of conducting virtual DMCA during the COVID-19 pandemic. METHODS: We conducted an online survey among health care providers who perform DMCAs in Alberta from March 2022 to February 2023. The survey consisted of 25 questions on demographics, preferences, and experience in conducting DMCAs virtually, and risks and barriers to doing virtual DMCAs. The data were analyzed using descriptive statistics. RESULTS: There were 31 respondents with a mean age of 51.1 (SD 12.7) years. The respondents consisted of physicians (45.2%, 14/31), occupational therapists (29%, 9/31), and social workers (16.1%, 5/31), with a majority (93.6%, 29/31) based in Edmonton. The mean number of years of experience conducting DMCAs was 12.3 (SD 10.7), with a median of 8 DMCAs (IQR 18.5) conducted per year. Most respondents conduct capacity interviews, with a majority (55.2%, 16/29) being associated primarily with acute care services. Furthermore, 54.8% (17/31) were interested in conducting DMCAs virtually; however, only 25.8% (8/31) had administered DMCAs virtually. Barriers and facilitators to virtual DMCAs relate to patients' characteristics and environment (such as communication difficulties, hearing or visual impairment, language barriers, ease of use of technology, or cognitive impairment), technology and technical support (need for technical support in both the client's and assessor's sides, the unreliability of internet connection in rural settings, and the availability of high-fidelity equipment), and assessors' ability to perform DMCA's virtually (ability to observe body language, interact with the client physically when needed, and build rapport can all be affected when conducting a DMCA virtually). In terms of implications for clinical practice, it is recommended that the patient or caregiver be familiar with technology, have a stable internet connection, use a private room, not be recorded, use a standardized assessment template, and have a backup plan in case of technical difficulties. CONCLUSIONS: Conducting DMCAs virtually is a relatively infrequent undertaking. Barriers and facilitators to adequate assessment need to be addressed given that virtual assessments are time-saving and expand reach.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".