The Development of a Logic Model to Guide the Planning and Evaluation of a Navigation Center for Children and Youth with Complex Care Needs
Bibliographic record
Abstract
Most systems across health settings and sectors are not well integrated and do not provide the needed supports, resources, or access caregivers and families require to properly care for their child with complex care needs (CCNs). NaviCare/SoinsNavi is a research-based navigation center aimed to help facilitate more convenient and integrated care to support the needs of children, youth, and their families using a patient navigator to offer personalized family-centered care. A logic model was developed by the NaviCare team to facilitate the foundational work needed for a successful program, such as program planning; establishing program goals and objectives; as well as providing a logical illustration of how the program will work. This visual representation of the assumed cause-and-effect connections between program components and desired outcomes informed the identification of inputs, activities, and outputs deemed critical for successful program execution, and for the research and evaluation of the program processes, as well as the program as a whole. This provided a safeguard to ensure critical activities were not overlooked, allowed the comparison of the ideal versus the realities of the program, enhanced communication, and highlighted data and resources that are needed for program implementation and evaluation. This article describes the development of NaviCare/SoinsNavi’s logic model, including how this framework will be used to guide the planning and evaluation of the navigation center to support achieving its vision that every child and youth with CCNs have access to the required health, social, and education services they require in a timely manner.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".