Guiding Documents for Engaging with Remote Chronic Disease Management Programs as a Healthcare Provider: A Scoping Review
Bibliographic record
Abstract
Introduction: Chronic disease management programs (CDMP) that include education and exercise enhance outcomes and reduce healthcare costs. Remote CDMP have the potential to provide convenient, cost-effective, and accessible options for individuals, but it is unclear how to best implement programs that include education and exercise. This review identified and synthesized resources for implementing remote CDMP programs that incorporate education and exercise. Methods: Peer-reviewed and grey literature were systematically searched from January 1998 to May 2022. Covidence software was used for screening and extraction. The data were synthesized and presented in a narrative and tabular format. Results: Six peer-reviewed manuscripts and six grey literature documents published between 2006-2022 were included. All resources described individual programs targeting various chronic conditions. Provider training, consent, participant screening, and safety considerations were identified. Conclusions: Guidelines for remote CFMP programs are lacking. Additional work is needed to design remote CDMP guidelines incorporating education and exercise.
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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 | 0.043 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".