The Team Assessment of Self-Management Support (TASMS): A new approach to uncovering how teams support people with chronic conditions
Bibliographic record
Abstract
Canadian and other healthcare systems are adopting primary care models founded on multidisciplinary, team-based care. This paper describes the development and use of a new tool, the Team Assessment of Self-Management Support (TASMS), designed to understand and improve the self-management support teams provide to patients with chronic conditions. Team Assessment of Self-Management Support captures the time providers spend supporting seven different types of self-management support (process strategies, resources strategies, disease controlling strategies, activities strategies, internal strategies, social interactions strategies, and healthy behaviours strategies), their referral patterns and perceived gaps in care. Four unique features make TASMS user-friendly: it is patient-centred, it uses provider-level data to create a team profile, it has the ability to be tailored to needs (diagnosis and visit type), and visual presentation of results are quickly and intuitively understood by both providers and planners. Currently being used by providers and planners in Nova Scotia, scaling up will allow more widespread use.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".