How do navigation programs address the needs of those living in the community with advanced, life-limiting Illness? A realist evaluation of programs in Canada
Bibliographic record
Abstract
BACKGROUND: We sought to identify innovative navigation programs across Canadian jurisdictions that target their services to individuals affected by life-limiting illness and their families, and articulate the principal components of these programs that enable them to address the needs of their clients who are living in the community. METHODS: This realist evaluation used a two-phased approach. First, we conducted a horizon scan of innovative community-based navigation programs across Canadian jurisdictions to identify innovative community-based navigation programs that aim to address the needs of community-dwelling individuals affected by life-limiting illness. Second, we conducted semi-structured interviews with key informants from each of the selected programs. Informants included individuals responsible for managing and delivering the program and decision-makers with responsibility and/or oversight of the program. Analyses proceeded in an iterative manner, consistent with realist evaluation methods. This included iteratively developing and refining Context-Mechanism-Outcome (CMO) configurations, and developing the final program theory. RESULTS: Twenty-seven navigation programs were identified from the horizon scan. Using specific eligibility criteria, 11 programs were selected for subsequent interviews and in-depth examination. Twenty-three participants were interviewed from these programs, which operated in five Canadian provinces. The programs represented a mixture of community (non-profit or volunteer), research-initiated, and health system programs. The final program theory was articulated as: navigation programs can improve client outcomes if they have supported and empowered staff who have the time and flexibility to personalize care to the needs of their clients. CONCLUSIONS: The findings highlight key principles (contexts and mechanisms) that enable navigation programs to develop client relationships, personalize care to client needs, and improve client outcomes. These principles include staff (or volunteer) knowledge and experience to coordinate health and social services, having a point of contact after hours, and providing staff (and volunteers) time and flexibility to develop relationships and respond to individualized client needs. These findings may be used by healthcare organizations - outside of navigation programs - to work towards more person-centred care.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".