Exploring the Role of Nurse Navigators as System Navigators in Primary Care
Bibliographic record
Abstract
Context Much of primary care in Ontario, Canada is delivered by small practices unaffiliated with teams, resulting in inequities in access to healthcare resources. The Seamless Care Optimizing the Patient Experience (SCOPE) program supports solo Primary Care Clinicians (PCC) by integrating nurse navigators who facilitate referrals, bridge regional healthcare resources, and improve care coordination. Objective This study explored the roles and collaborative practices of SCOPE nurse navigators across the program’s participating sites, emphasizing how collaboration supports care delivery. Study Design and Analysis A multi-method design included an online survey and two focus groups. Survey data were analyzed using descriptive statistics, while focus group data were thematically analyzed using a qualitative descriptive methodology. Setting or Dataset The study was conducted across all SCOPE program sites in Ontario where nurse navigators were employed. Population studied Sixteen SCOPE nurse navigators were invited to participate; twelve participated in the surveys and focus groups. Intervention/instrument The survey explored nurse navigators’ roles, clinical background, and job satisfaction. Focus groups explored collaborative practices, barriers to cross-site communication, and examples of shared problem-solving across SCOPE sites. Outcome Measures Measures included self-reported job satisfaction, frequency and method of cross-site communication, and qualitative themes related to collaboration and system navigation. Results Participants reported diverse clinical backgrounds and high job satisfaction. They maintained regular contact with colleagues using virtual tools and bi-monthly meetings to expedite referrals and share resources. Four key themes emerged: 1) supporting underserved communities by bringing in resources, 2) connecting PCCs and specialists living in different regions, 3) bridging resources between regions, and 4) ensuring timely care for vulnerable patients alleviate emergency department strain. Conclusions SCOPE nurse navigators enhance care coordination and reduce disparities by collaborating across sites. Their collaboration and resource sharing improve care access and outcomes. This study highlights how non-physician team members can extend primary care capacity, support clinicians, and deliver more equitable care. Expanding similar integrated care models may strengthen primary care for underserved populations.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".