The Role of Personal Health Record Systems in Chronic Disease Management
Bibliographic record
Abstract
Chronic illnesses account for the largest portion of healthcare spending in Canada; they are the leading cause of premature death. As a result, healthcare organizations are focused on improving both health and financial outcomes. Addressing chronic illnesses involves more frequent and impactful interactions with both current patients and those at risk of developing a chronic condition. This transformation requires that healthcare organizations shift from a system based solely on in-person interactions to one that leverages digital solutions that support interactions regardless of the patients' location. Personal health record systems (PHRS) can facilitate patients' access to their health data at any time of the day, anywhere in the world. PHRS also offers a myriad of features to help providers' engage, educate and empower patients to make proactive and preventive care a reality. Discussed in this paper are the ways in which PHRS can support the optimal management of chronic conditions and the current barriers to widespread adoption.
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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 | 0.045 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".