Telehomecare for Vulnerable Populations: The Evaluation of New Models of Care
Bibliographic record
Abstract
The use of telehomecare has the potential to facilitate access to healthcare services for vulnerable populations. However, evidence on the implications of telehomecare on various aspects related to patients, healthcare professionals, organizations, and healthcare systems is still limited. Assessing the various effects of telehomecare for these different groups of stakeholders is thus an essential step to ensure future integration of this technology into mainstream healthcare services. A synthesis of lessons learned from the evaluation of three telehomecare experimentations targeting specific vulnerable groups is proposed. This paper presents the various models that were implemented to assess telehomecare services for vulnerable populations, explores issues related to conducing telehomecare evaluations, and provides a reflection on key factors that might influence the success of telehomecare projects. Lessons learned from these three experimentations provide valuable insights to orient the development of telehomecare services for various vulnerable groups in the population.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".