From experience to a learning health system: peer-to-peer perspectives and implications for healthcare navigation in Alberta, Canada
Bibliographic record
Abstract
Background Healthcare navigation services help individuals access timely and appropriate care within complex health systems, particularly those facing systemic and equity-related barriers. Understanding navigation experiences is essential to addressing service gaps and improving health outcomes. This study sought to examine the lived experiences of navigation in Alberta to identify inequities within existing programs and to provide recommendations for strengthening person-centered navigation within a learning health system framework. Materials This was a qualitative, peer-to-peer, patient-oriented research study. The study design followed the Patient and Community Engagement Research process of SET-COLLECT-REFLECT. The SET phase engaged patient and public partners in discussions to co-design the research question and the study design. The COLLECT phase included focus groups and interviews with adult residents in Alberta who had been navigated ( n = 13) and those who had experience as healthcare navigators ( n = 13) in the Alberta healthcare system. The data were thematically analyzed, identifying key themes and subthemes. The REFLECT phase ran two focus groups with COLLECT participants for member checking. This approach yielded the recommendations. Results Of the 26 participants, over 75% were women (77% of the Navigated group and 75% of the Navigator group) aged 41–50 years old. Half of those in the Navigator group had provided their service for more than 5 years and had received specialized training in healthcare navigation. The following themes were identified: (1) participants’ situations and circumstances, (2) navigation experiences, (3) perspectives, (4) need for healthcare navigators, (5) the navigator role, (6) current best practices and challenges, and (7) training and support. Five recommendations included expanding the scope and enhancing awareness of navigation programs with a personalized approach and embedded evaluation and developing and formalizing navigation training programs. Conclusion This study identified gaps and opportunities in healthcare navigation programs from both navigator and navigated perspectives. The findings provide patient-centered recommendations to strengthen navigation services and their integration into Alberta's learning health system that can enhance equitable access, healthcare experiences, and outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".