Exploring Patient Understandings of Navigation Services Within Alberta's Healthcare System: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Patient navigation was first envisioned to assist marginalized cancer patients access timely healthcare services by identifying and addressing social barriers to care. While this understanding of patient navigation may still hold for a subgroup of programs today, its expansion over the past 30 years has resulted in a diverse set of interventions with distinct care settings, patient eligibility criteria, navigator training requirements and program goals. This study aimed to explore patients' understanding of patient navigation programs to identify program features that are of particular value and importance to them. METHODS: In this qualitative study, we conducted one-on-one semi-structured interviews from November 2023 to February 2024 with patients involved in five distinct hospital-, clinic- and community-based patient navigation programs across Alberta. Inductive thematic analysis and interpretive exercises were performed to construct a coherent narrative relevant to the research objective. Study participants were adult patients with patient navigation program exposure for at least 1 month (range: 2 months to 11 years). RESULTS: Twenty-three patient experiences were captured in the study (12 [52%] women; median [IQR] age, 59 [48-67] years), with approximately half receiving support from a nurse navigator (11/23, 48%). Regardless of navigation type, the patients' stories were tethered by their navigators' provision of personalized, seamless and humanized care. These perceived navigator functions were accomplished through patient-identified navigator characteristics, including navigator approachability, accessibility and comprehensive systems knowledge. While the identified functions and characteristics of navigators were consistent across patients, the operationalization of these components varied based on the program's setting and the particular needs of each patient. CONCLUSIONS: The commonalities in patient perceptions of patient navigation indicate continued points of overlap across programs despite their increasing heterogeneity. Additionally, our findings provide insight into the functions and characteristics of patient navigation most valued by patients, which may inform future program development and implementation efforts. PATIENT AND PUBLIC CONTRIBUTION: Continued collaboration with two patient partners was maintained throughout the study to ensure responsiveness to patient priorities.
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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) | 0.000 | 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".