Integrated Care Pathway (ICP), an Inter-professional Outline of Evidence Informed Care in Mental Health and Addictions: an Innovative Treatment Approach From Concept to Results
Bibliographic record
Abstract
One of the challenges in mental health is the overwhelming number of clinical guidelines, yet the apparent lack of standardization in treatment. Patients expect that clinical decisions will be made on the basis of evidence with access to standardized treatment. The mental health system is constantly challenged by simultaneous needs to both improve quality of care and increase efficiency, without any significant increase in staffing or funding. ICP is the innovative solution to address these challenges. The Centre for Addiction and Mental Health (CAMH) ICPs has three key components: a specific medication algorithm, non-pharmacological interventions, and team effectiveness interventions. Our methodology to develop ICPs includes: evidence reviews, process redesign and knowledge translation. An ICP specifically details what to do, when to do it and who will do it. Three ICP pilots have been implemented and currently 100 patients have been treated and are being evaluated. The overall intent of the initiative is to develop practical ways of changing the delivery of services to improve patient experience and outcomes. The benefits of forming an ICP have manifested themselves early in development. The discussions that have taken place during the development of the pathway have encouraged closer team working alliances and a greater appreciation of existing treatment variability. This presentation will review the lessons learned in the development, implementation and evaluation of ICPs at the (CAMH), including early findings from our teams and early outcomes from patients. Key elements of success that are fundamental components of a pathway will be demonstrated.
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 | 0.053 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".