Experiences of Caregivers as Clients of a Patient Navigation Program for Children and Youth with Complex Care Needs: A Qualitative Descriptive Study
Bibliographic record
Abstract
The number of Canadian children and youth with complex care needs has continued to rise, and their need for resources across all sectors can be extensive. Navigating the maze of resources and services can create confusion and impact how care is delivered and integrated. Patient navigators can help support and guide patients and caregivers through the healthcare system by matching their needs to appropriate resources with the aim to improve access and promote the integration of care. This qualitative study explored caregivers' experiences caring for a child or youth with complex care needs, and their experiences and satisfaction as clients of a patient navigation centre. Participants included 22 clients from NaviCare/SoinsNavi, a patient navigation centre in Canada for children and youth with complex care needs and their families. Three main themes emerged: 1) caring for a child or youth with complex care needs, 2) navigating the system, and 3) the value of patient navigation. Findings suggest caregivers caring for a child or youth with complex care needs often feel overwhelmed, fearful, and alone; yet, patient navigation can be an innovative approach to support their needs through facilitating more convenient and integrated care, and improving access to education, supports, and resources.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".