Cancer Patient Navigation in Canada: Directions From the North

OBJECTIVES

Cancer is a complex disease that is experienced by those affected by cancer and their loved ones differently. The importance of cancer patient navigation is quintessential to support those affected through the healthcare system and to supportive resources. Canadian cancer statistics advise of the continued increase of cancer and impacts on health care. With Canada being a large geographical area, large portions of the population live in rural and remote areas with decreased access to health services. In Canada, cancer navigation is different across the country; each province's or territory's health authority creates their own cancer navigation program based on the needs of their patients. This report aims to provide an overview of cancer in Canada, along with the different navigation programs available nationally. Additionally, it will review the role the Canadian Association of Nurses in Oncology/Association canadienne des infirmières en oncologie (CANO/ACIO) plays in creating a community of practice to support cancer patient navigators across the country.

METHODS

The information on various provincial and territorial navigation programs was obtained through discussion with the CANO/ACIO Navigation Special Interest Group (SIG). All provinces and territories were interviewed with the exception of Quebec, Prince Edward Island, Nunavut, and Yukon.

RESULTS

While the vast majority of navigation has a similar core intent, there are many differences between the provinces and territories in the navigation programs. These differences are based on geographical need and the individual health authorities.

CONCLUSIONS

The Canadian Association of Nurses in Oncology/Association canadienne des infirmières en oncologie (CANO/ACIO) provides a community for cancer navigators to connect through a Special Interest Group (SIG), meeting virtually monthly to support each other across Canada to collaborate, identify issues, trends, and challenges.

IMPLICATIONS FOR NURSING PRACTICE

Cancer patient navigation is a valuable resource for all individuals with cancer and their loved ones, particularly when faced with difficulties accessing care in rural and remote areas. Cancer patient navigators' scope is similar in intent, despite potential differences in programs. By connecting with other navigators through the CANO/ACIO navigation SIG, navigators across the country can provide a connection to discuss program similarities and barriers and opportunities for cancer navigation programs to work together to support each other and evolve their programs to meet the needs of their provincial and territorial residents.

Patient Satisfaction with Navigator Interpersonal Relationship (PSN-I): item-level psychometrics using IRT analysis

BACKGROUND

Patient navigation (PN) is a promising intervention to eliminate cancer health inequities. Patient navigators play a critical role in the navigation process. Patients' satisfaction with navigators is important in determining the effectiveness of PN programs. We applied item response theory (IRT) analysis to establish item-level psychometric properties for the Patient Satisfaction with Interpersonal Relationship with Navigators (PSN-I).

METHODS

We conducted a confirmatory factor analysis (CFA) to establish unidimensionality of the 9-item PSN-I in 751 cancer patients (68% female) between 18 and 86 years old. We fitted unidimensional IRT models-unconstrained graded response model (GRM) and Rasch model-to PSN-I data, and compared model fit using likelihood ratio (LR) test and information criteria. We obtained item parameter estimates (IPEs), item category/operating characteristic curves, and item/test information curves for the better fitting model.

RESULTS

CFA with diagonally weighted least squares confirmed that the one-factor model fit the data (RMSEA = 0.047, 95% CI = 0.033-0.060, and CFI ≈ 1). Responses to PSN-I items clustered into the 4th and 5th categories. We aggregated the first three response categories to provide stable parameter estimates for both IRT models. The GRM fit the data significantly better than the Rasch model (LR = 80.659, df = 8, p < 0.001). Akaike's information coefficient (6384.978 vs. 6320.319) and Bayesian information coefficient (6471.851 vs. 6443.771) were lower for the GRM. IPEs showed substantial variation in items' discriminating power (1.80-3.35) for GRM.

CONCLUSIONS

This IRT analysis confirms the latent structure of the PSN-I and supports its use as a valid and reliable measure of latent satisfaction with PN.