117 Enhancing professional truck drivers‘ experience with virtual care: understanding challenges of shared decision making to improve self- management of chronic diseases
Bibliographic record
Abstract
Introduction While, professional truck drivers (PTD) have a higher risk of chronic conditions and mental health issues, most of them have a low level of education, lower health literacy and lower compliance with medical advice and medication intake. In collaboration with the trucking industry, we co-designed a tailored digital health primary care to improve PTD health outcomes and satisfaction with care. Key aspects of our patient engagement platform are remote patient monitoring, access to integrated interprofessional services and shared decision making (SDM). SDM can be challenging for patients with lower levels of education health literacy and eLiteracy. The aim of this study is to better understand PTD’s experience to inform our continuing improvement strategy and adapt our SDM approaches. Methods A grounded theory based qualitative design using semi- structured interview to collect data. In-depth interviews were conducted with 21 PTD based in a Canadian province. The majority (95%) were men, 50% were Canadian citizens, 75% had a least one chronic disease, and 37% had completed primary school. The mean age was 46.5 yo (Sd = 9.64 [27:68]). Results Through analysis of the recorded interviews (using a software), themes of patient-health professional relationship (i.e. trust, non- judgmental, compassion and repetition), tailored health education (i.e. verbal, written and recorded), integrated and personalized care, and timely access to care emerged as major influences on the SDM experiences of PTD. Barriers to effective communication (i.e. incongruency of information between health providers, slow or lack of health outcomes improvement, and work-related conditions) were also revealed. Conclusion(s) Lack of timely access to primary care and support for SDM likely negatively affect the health of PTD. Our findings shed light on how to support health professionals and PTD, and provide appropriate and adapted health resources to support SDM among PTD.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".