Health TAPESTRY Ontario: Protocol for a Randomized Controlled Trial to Test Reproducibility and Implementation
Bibliographic record
Abstract
Abstract Background: Health TAPESTRY (Health Teams Advancing Patient Experience: STRengthening qualitY) aims to help people stay healthier for longer where they live by providing person-focused care through the integration of four key program components: 1) trained volunteers who visit clients in their homes, 2) an interprofessional primary health care team, 3) use of technology to collect and share information, and 4) improved connections to community health and social services. The initial randomized controlled trial of Health TAPESTRY found promising results in terms of health care use and patient outcomes, indicating a shift from reactive to preventive care. The trial was based on one clinical academic centre, thus limiting generalizability. The study objectives are: 1) to test reproducibility of the established effectiveness of Health TAPESTRY on physical activity and hospitalizations, 2) to test the feasibility of, and understand the contributing factors to, the implementation of Health TAPESTRY in six diverse communities across Ontario, Canada, and 3) to determine the value for money of implementing Health TAPESTRY. Methods : This planned study is a pragmatic parallel randomized controlled trial with a delayed intervention for control participants at six months. This trial will simultaneously assess effectiveness and implementation in a real world setting (Type II hybrid) in six diverse communities across Ontario. Participants 70 years of age and older will be randomized into the Health TAPESTRY intervention or the control group (usual care). Intervention clients will receive an individualized plan of care from an interprofessional care team. The plan will be based on a client’s goals and current health risks identified through volunteer visits. The study’s outcomes are mapped onto the RE-AIM framework, with levels of physical activity and number of hospitalizations as the co-primary outcomes. The main analysis will be a comparison at six months. Discussion: It is important to evaluate the effectiveness and implementation of Health TAPESTRY in multiple communities prior to scaling or widespread adoption. Trial registration: ClinicalTrials.gov: NCT03397836 (posted January 12, 2018). Keywords: Primary health care, Randomized controlled trial, Integrated care, Interdisciplinary health care teams, Health care volunteers, Older adults, Implementation
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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 | 0.093 | 0.077 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.083 | 0.013 |
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".