Professional Navigation Framework: Elaboration and Validation in a Canadian Context
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To elaborate, refine, and validate the professional navigation framework in a Canadian context. RESEARCH APPROACH: A two-step approach consisting of a qualitative evaluative design and formal consultations. SETTING: Two applications of professional navigators in Quebec and Nova Scotia, Canada. PARTICIPANTS: Patient navigators, medical oncology specialists, nurses and oncology staff, administrators, family physicians, patients with cancer, and patients' families and significant others. METHODS: Individual interviews (n = 49) and focus groups (n = 10) were conducted with professional navigators, patients and family members, front-line staff, family physicians, and health administrators. Formal consultations (n = 13) occurred with clinical experts, managers, and researchers from across Canada. MAIN RESEARCH VARIABLES: The interview guide was based on an evaluative conceptual framework integrating questions related to the implementation process of the role of professional navigators and their organizational and clinical functions. FINDINGS: Results support a bi-dimensional framework and define key role functions. The first dimension, health system-oriented, refers to continuity of care. The second dimension, patient-centered, corresponds to empowerment. For each dimension, related concepts were illustrated from data. Examples of outcomes also were suggested. CONCLUSIONS: The framework brings clarity to the role and functions of professional navigators and suggests relevant outcomes for program evaluations. INTERPRETATION: With a clear definition of their role, professional navigators may be more efficient and less challenged in terms of setting priorities and making decisions while having to face demands from the health system and patients. The integrative framework could improve the effectiveness of cancer navigation programs.
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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 | 0.046 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".