Experiences of Motivational Interviewing in Virtual Health-care Visits for Adults With Type 2 Diabetes Mellitus: A Qualitative Analysis
Bibliographic record
Abstract
OBJECTIVES: The purpose of this qualitative study is to identify barriers minimizing the effectiveness of motivational interviewing during virtual clinic encounters for individuals with type 2 diabetes based on the capability, opportunity, motivation, and behaviour (COM-B) model. METHODS: One-on-one semistructured interviews were conducted from March to June 2023, with 17 adults with type 2 diabetes (64.7% female; median age 69 years [range 47 to 83 years]) followed at St. Michael's Hospital (Toronto, Canada). Themes from transcribed interviews were identified through descriptive analysis using a grounded theory approach. RESULTS: The following main themes were identified: 1) face-to-face appointments strengthen provider-patient rapport and collaboration; 2) virtual encounters reduce patient accountability and hinder health-seeking behaviour; and 3) individuals with physical disabilities and/or low technological proficiency experience decreased provider accessibility. Protective factors that can mitigate these negative impacts include establishing rapport during in-person appointments before transitioning to virtual appointments and incorporating a video component during virtual encounters. CONCLUSIONS: Several barriers of virtual appointments currently limit the effectiveness of motivational interviewing for individuals with type 2 diabetes and make it difficult to provide person-centred care, especially by phone. However, there are protective factors that help to maintain healthy lifestyle behaviours, even after transitioning to virtual settings, and are areas for optimization moving forward.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".