L'utilisation des télésoins à domicile pour un meilleur suivi des maladies chroniques
Bibliographic record
Abstract
AIM: The purpose of this study was to understand how home telecare technologies can be used to improve services for people with chronic diseases. METHODS: Canadian elders with at least one of the targeted chronic diseases (COPD, heart failure, hypertension, diabetes) were asked to use telehomecare equipment. The data needed to assess the implementation process and to monitor outcomes were collected through participatory observation, documentary analysis and interviews. RESULTS: The study found that the technology has a number of benefits for patients, particularly in terms of access to health services. By enabling patients to access more information about their health, the use of the technology, combined with an educational program, contributes to increasing their capacity for self-management. The results also indicate that the telehomecare equipment had a positive impact on clinical decision-making. By facilitating health professionals' access to information and expertise, it was found to promote interprofessional practice. The study found that telehomecare technology has an organizational impact on practice and requires organizational adaptation, the form of which will depend on local organizational and clinical settings. CONCLUSION: The results suggest that telehomecare technology helps to create conditions that need to be met by health care organizations in order to improve service delivery to people with chronic diseases, particularly with regard to interprofessional collaboration, health professionals' access to information and expertise and active patient participation. However, the successful implementation of the technology requires a detailed analysis of the settings in which it is used.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".