Barriers and Facilitators to eHealth Technology Use Among Community-Dwelling Individuals With Spinal Cord Injury: A Qualitative Study
Bibliographic record
Abstract
Background: As eHealth technologies become a more prevalent means to access care and self-manage health, it is important to identify the unique facilitators and barriers to their use. Few studies have evaluated the use or potential use of eHealth technologies in spinal cord injury (SCI) populations. Objectives: The primary objective of this study was to explore and identify barriers and facilitators to engagement with eHealth technologies among individuals with SCI. Methods: A qualitative descriptive study was conducted. Data were collected via one-on-one, semi-structured interviews with a subsample of 20 community-dwelling participants enrolled in a larger clinical trial. Analysis of the transcripts was undertaken using a four-phase process of content analysis. Results: Our analysis identified three barriers to engagement with eHealth technologies, including (1) overcoming a digital divide to comprehending and utilizing eHealth technologies, (2) navigating internet resources that provide too much information, and (3) interacting with these technologies despite having limited hand function. Our analysis also identified three facilitators to using eHealth technologies, including (1) having previous successful experiences with eHealth technologies, (2) being able to use voice activation features, and (3) being able to interact in an online community network. Conclusion: By exploring barriers and facilitators to eHealth technology use, these findings may have a short-term impact on informing researchers and clinicians on important factors affecting engagement of individuals with SCI with telemedicine, mobile, and web applications (apps) and a long-term impact on informing future development of eHealth interventions and tools among chronic disease populations.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".