Assessing readiness to implement patient navigator programs in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVE: To explore factors influencing the implementation of patient navigator programs within a hospital for seniors with complex care needs. METHODS: A qualitative descriptive design using in-depth interviews was conducted. Participant interviews were conducted in Toronto, Ontario between September 2020 and February 2021. Data were analysed using thematic analysis. RESULTS: Thirty-five semi-structured interviews were conducted with 38 participants from a large urban hospital (n = 21) and community healthcare organizations (n = 17), including organizational leaders, and acute care and rehabilitation providers. Follow-up interviews occurred with 16 participants (7 from the community and nine from the hospital). This study identified five key factors influencing organizational readiness for successful implementation of a patient navigator program for seniors with complex conditions, which included: (a) vision from senior leadership, (b) technological infrastructure, (c) existing hospital-community partnerships, (d) well-established process for referrals, and (e) staff capacity. The overarching theme of communication was also identified. CONCLUSIONS: The findings of this study provide a better understanding of hospital and community professionals' needs and challenges when implementing patient navigator programs for seniors with complex care conditions. There are a number of factors that influence an organization's readiness for program uptake and delivery, with the need for clear communication being paramount. Further research to test the effects of readiness on successful implementation outcomes is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".