Patients and families experiences with video telehealth in rural/remote communities in Northern Canada
Bibliographic record
Abstract
AIM: To explore patients' and families' experiences with video telehealth consultations as a method of health care delivery in rural/ remote communities in Northern Canada. BACKGROUND: Accessing health services in isolated populations where human resources and infrastructure are constrained by vast geographical landmasses poses challenges and opportunities for nurses, health care providers, patients and families. DESIGN: A qualitative approach was adopted with a purposeful sample of 10 patients and four family members representative of nine communities. METHOD: Selection criteria included patients receiving telehealth visits for a minimum of a year and willing to share their experiences. Data were collected during the winter of 2006 using semi-structured video taped interviews and analysed using a qualitative thematic content analysis. RESULTS: Patients and families experiences of their telehealth visits centered on three key themes: lessening the burden (costs of travel, accommodations, lost wages, lost time and physical limitations), maximising supports (access to family, friends, familiar home environment, nurses and other care providers), tailoring specific e-health systems to enhance patient and family needs. CONCLUSION: The benefits of telehealth extend not only to patients and families but are linked to benefits for providers as well as the health care system. RELEVANCE TO CLINICAL PRACTICE: This study indicates that video telehealth is an effective mechanism for delivering nursing and other health services to rural/remote communities and can impact positively on the quality of health care. The integration of telehealth practice can enhance the coordination, organisation and implementation of health care services.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".