Moving towards Systems and Design Thinking through Implementation Science.
Bibliographic record
Abstract
Systems Improvement through Service Collaboratives (SISC) is an initiative within Open Minds, Healthy Minds: Ontario’s Comprehensive Mental Health and Addictions Strategy, a ten year plan that commits to transformation of mental health and addiction services for all Ontarians. Within the first three years of the SISC initiative, 18 Service Collaboratives facilitated local systems change to better support individuals with mental health and addictions needs. The initiative is sponsored by the Provincial System Support Program at the Centre for Addiction and Mental Health. The SISC initiative, used a strategic framework (Implementation Science) to guide a geographically dispersed, cross-sector and community-led systems change process in mental health and addictions. This experience has highlighted some integration with systems and design thinking. When utilized effectively, The Implementation Science framework provides an evidence-informed process to guide intentional, actionable change,. We can look to innovative large-scale initiatives, like SISC, that have tested and adapted approaches in a variety of contexts to better understand how to fully realize the value of integrating these frameworks into evolving system design practices
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.137 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".