Spanning Boundaries into Remote Communities: An Exploration of Experiences with Telehealth Chronic Disease Self-Management Programs in Rural Northern Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In rural and remote settings, providing education programs for chronic conditions can be challenging because of the limited access and availability of healthcare services. The purpose of this study was to explore the experiences of participants in a chronic disease self-management program via telehealth (tele-CDSMP) and to identify facilitators and barriers to inform future tele-CDSMP delivery models. MATERIALS AND METHODS: Nineteen tele-CDSMP courses were delivered to 13 Northern Ontario (Canada) communities. Two types of group were delivered: (1) single telehealth site (one community formed a self-management group linked to program leaders via telehealth) and (2) multiple telehealth sites (several remote communities were linked to each other and program leaders via telehealth). Following the completion of the courses, participants were invited to partake in a focus group. RESULTS: Overall, 44 people participated in the focus groups. Four main themes were identified by tele-CDSMP participants related to the overall experience of the program: (1) bridging the access gap, (2) importance of group dynamics, (3) importance of strong leaders, and (4) preference for extended session time. Key barriers were related to transportation, lack of session time, and access to Internet-based resources. The main facilitators were having strong program leaders, encouraging the development of group identity, and providing enough time to be comfortable with technology. CONCLUSIONS: Our findings suggest overall the tele-CDSMP was a positive experience for participants and that tele-CDSMPs are an effective option to increasing access to more geographically isolated communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".