Exploring care coordination in primary and community care for children and families who experience social and economic vulnerabilities: protocol of a rapid scoping review
Bibliographic record
Abstract
To support care coordination for children and families experiencing social or economic vulnerabilities in the context of Canadian primary care, there is a need to understand existing frameworks and approaches to care coordination for this population. This rapid scoping review aims to map the field of literature on care coordination in primary and community care for these families and provide a critical overview of frameworks used in this context. We aim to identify care coordination frameworks, models, and approaches in the context of primary and community care to address the following research questions: 1. How does current literature conceptualize care coordination for children and families experiencing social or economic vulnerabilities in a primary or community care setting? 2. What paediatric population have these frameworks been developed for (e.g., specific age, illness/condition, or level of complexity) 3. Who has been involved in developing these frameworks or models? 4. How has care coordination been applied, including the key principles, setting, activities and personnel involved?
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 | 0.158 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".