Inter-Establishment Complex Musculoskeletal Care Pathways in Montreal: Timeline of a Collaboration Involving a Research Team Within a Continuous Quality Improvement Initiative
Bibliographic record
Abstract
Children and adolescents with complex musculoskeletal conditions may receive health care that requires at least 1 transfer between 4 specialized pediatric establishments in the Montreal region (Québec, Canada). This may result in challenges in navigating the system. A collaborative approach, aiming to make the inter-establishment care pathways seamless and to improve the integration of musculoskeletal health services, brought together key stakeholders including a research team. The aim of this paper is to describe the timeline of the collaborative approach's key milestones and activities and, more specifically, to describe the context, process, and outputs of the involvement of researchers in support of a continuous quality improvement project based on an integrated approach. The descriptive timeline was constructed from a qualitative document analysis of the project-related gray literature (n = 80 documents) and was validated and interpreted with key stakeholders. The results showed how the collaborative project was set up and operated, as well as what solutions were developed and implemented. The strategies on how the research team was involved in the integrated approach in addition to its research activities were also described. Conclusions suggest practice recommendations for creating change processes by integrating research, service evaluation and clinical audit into quality improvement projects.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".