Navigation programs to support community-dwelling individuals with life-limiting illness: determinants of implementation
Bibliographic record
Abstract
Abstract Background As the Canadian population ages and the prevalence of chronic illnesses increases, delivering high-quality care to individuals with advanced life limiting illnesses becomes more challenging. Community-based navigation programs are a promising approach to address these challenges, but little is known about how these programs are successfully implemented to meet the needs of this population. This study sought to identify the key determinants that contribute to the successful implementation of these programs within Canada. Methods A qualitative study was undertaken to understand the implementation of eleven innovative, community-based navigation programs that aim to address the needs of individuals with life-limiting illnesses as they approach the end of life. The Consolidated Framework for Implementation Research (CFIR) guided the study design. Key informants (n = 23) within these programs took part in semi-structured interviews where they were asked to discuss how these programs are implemented. Data were analyzed using techniques employed in qualitative description. Results We identified key determinants of successful implementation within each CFIR domain. In the outer setting domain, participants emphasized the importance of filling gaps in care to meet client needs, developing strong relationships with clients and community-based organizations, and navigating relationships with healthcare providers. At the inner setting level, leadership support, staff compatibility, and available resources were identified as important factors. In terms of intervention characteristics, the ability to adapt was cited as a facilitator, whereas costs were identified as a barrier. For the characteristics of individuals, participants described the importance of having staff whose values align with the program, and who have the experience and skills necessary to work with complex clients. Finally, having strong champions and evaluation processes were highlighted as important process-oriented determinants of successful implementation. Conclusion This study provides valuable insights into the determinants of successful implementation of community-based navigation programs in Canada. Understanding these determinants can guide the future development and integration of navigation programs to successfully meet the needs of those with life-limiting illnesses.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".