Rural-Urban Comparison of Telehome Monitoring for Patients with Chronic Heart Failure
Bibliographic record
Abstract
INTRODUCTION: Rural geographic isolation may act as a promoting or restraining variable to the diffusion of technology and healthy aging in the community. Telehome monitoring (TM) leverages technology to support seniors living in the community with chronic conditions. To date, limited research has investigated the utilization of TM in rural settings. This study assesses the comparative utilization of TM for patients with heart failure in rural versus urban environments. MATERIALS AND METHODS: We conducted a cross-sectional study involving chart reviews of all patients enrolled in the TM program at the University of Ottawa Heart Institute during 2014. Data were extracted on urban/rural status, demographic characteristics, and process and outcomes of care. Descriptive, bivariate, and multivariate analyses were conducted. RESULTS: More rural patients did not have a documented reason for emergency room visits compared to urban patients. There was no significant association between the urban/rural status and the process and outcome measures at the multivariate level. Being followed-up regularly by a family physician and a specialist, as opposed to a specialist or general practitioner only, was associated with significantly longer TM period and a higher number of diuretic adjustments and calls made by nurses. DISCUSSION: Although more urban patients were older and living alone, their profile did not affect their utilization of TM. The difference in diagnosis between urban and rural patients also did not contribute to such differences. Hence, there is no variation in the process and outcome measures associated with the utilization of TM between urban and rural environments. CONCLUSIONS: Rural patients may not be perceived as extensive users of resources nor patients who represent challenges in terms of feasibility of TM use.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".