Assessing the impact of a health navigator on improving access to care and addressing the social needs of palliative care patients experiencing homelessness: A service evaluation
Bibliographic record
Abstract
BACKGROUND: Health navigators are healthcare professionals who specialize in care coordination, case management, navigating transitions, and reducing barriers to care. There is limited literature on the impact of health navigators on community-based palliative care for people experiencing homelessness. AIM: We devised key performance indicators in nine categories with the aim to quantify the impact of a health navigator on the delivery of palliative care to patients experiencing homelessness. DESIGN: Data were collected prospectively for all patient encounters involving a health navigator from July 2020 to 2021 and reviewed to determine the distribution of the health navigator's role and the ways in which patient care was impacted. SETTING AND PARTICIPANTS: This study was conducted in Toronto, Ontario with the Palliative Education and Care for the Homeless (PEACH) Program. At any one time, the PEACH health navigator served a total of 50 patients. RESULTS: We identified five key areas of the health navigator role including (1) facilitating access (2) coordinating care (3) addressing social determinants of health (4) advocating for patients, and (5) counselling patients and loved ones. The health navigator role was split evenly between activities pertaining to palliative care for structurally vulnerable populations and community-based palliative care for the general population. To achieve high impact outcomes, a considerable investment of time and energy was required of the health navigator, speaking to the importance of adequate and sustainable funding. CONCLUSIONS: These findings underscore the potential for health navigators to add value to community-based palliative care teams, especially those caring for structurally vulnerable populations.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".