085 Implementing a Knowledge Application Program for Anxiety and Depression in Community-Based Primary Mental Health Care: The Clinical Decision Support Component
Bibliographic record
Abstract
Background We developed a knowledge application programme to support the improvement of the organisation and delivery of care for anxiety and depressive disorders in community-based primary mental health care teams (CMHTs) in Quebec (Canada). The programme is based on the Chronic Care Model, including a Decision Support component, and the PARiHS framework. Objectives 1- To implement and evaluate a knowledge application programme, 2- to explore barriers and facilitators associated with the implementation of Decision Support strategies, particularly the uptake of clinical practice guidelines. Methods The design is a mixed-methods prospective multiple case study, with data drawn from the two phases of the project (2008-2010; 2011-2014). Multidisciplinary local working committees in the six CMHTs were required to develop and implement local quality improvement plans with the support of a knowledge broker. Results While we observed barriers and facilitators at the clinician level in terms of knowledge (e.g familiarity) and attitudes (e.g. applicability, agreement), contextual factors (e.g resources, access) also played an important role in the uptake of clinical practice guidelines into the six local CHMTs. Discussion Decision Support is a central component of the Chronic Care Model that aims to promote clinical care that is consistent with scientific evidence. Implications The uptake of evidence in primary care is a complex process that requires careful consideration of the context in which innovations are introduced, and our assessment of barriers and facilitators can be relevant to other primary health care organisations seeking to increase the uptake of anxiety and depression clinical guidelines.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".