The relationship between treatment burden and the use of telehealth technologies among patients with chronic conditions: A scoping review
Bibliographic record
Abstract
Patients managing chronic conditions often face significant treatment burdens due to the long-term nature of their care. Treatment burden refers to the workload associated with the self-management of chronic conditions. While telehealth is commonly used to support these patients, there is a growing concern about its impact on marginalized patient populations. Specifically, we lack a comprehensive overview on how and what types of telehealth can increase or minimize the perceived treatment burden among this patient population. To synthesize evidence on the relationship between treatment burden and telehealth among patients with chronic conditions and their caregivers. We used Arksey and O'Malley's five-step scoping review framework to identify relevant literature that was published from January 2004 to May 2023. Fifty-four studies were included in the review. We identified various ways telehealth increases or minimizes patients’ treatment burden. Some of the patient-reported benefits of telehealth regarding treatment burden were reducing time and cost associated with travel to the clinics. Conversely, some burdens associated with telehealth were making sense of the large volume of complex data generated by health technologies, and the extra work required to set up and learn about new technology. Review findings emphasize the importance of considering the concept of treatment burden while introducing telehealth-based interventions to support patients and their caregivers with chronic conditions. Future research needs to identify how to minimize the treatment burden associated with telehealth while implementing new telehealth interventions.
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 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 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".