Implementation and maintenance of patient navigation programs linking primary care with community-based health and social services: a scoping literature review
Bibliographic record
Abstract
BACKGROUND: Since the early 90s, patient navigation programs were introduced in the United States to address inequitable access to cancer care. Programs have since expanded internationally and in scope. The goals of patient navigation programs are to: a) link patients and families to primary care services, specialist care, and community-based health and social services (CBHSS); b) provide more holistic patient-centred care; and, c) identify and resolve patient barriers to care. This paper fills a gap in knowledge to reveal what is known about motivators and factors influencing implementation and maintenance of patient navigation programs in primary care that link patients to CBHSS. It also reports on outcomes from these studies to help identify gaps in research that can inform future studies. METHODS: This scoping literature review involved: i) electronic database searches; ii) a web site search; iii) a search of reference lists from literature reviews; and, iv) author follow up. It included papers from Canada, the United States, the United Kingdom, Australia, New Zealand, and/or Western Europe published between January 1990 and June 2013 if they discussed navigators or navigation programs in primary care settings that linked patients to CBHSS. RESULTS: Of 34 papers, most originated in the United States (n = 29) while the remainder were from the United Kingdom, Canada and Australia. Motivators for initiating navigation programs were to: a) improve delivery of health and social care services; b) support and manage specific health needs or specific population needs, and; c) improve quality of life and wellbeing of patients. Eleven factors were found to influence implementation and maintenance of these patient navigation programs. These factors closely aligned with the Diffusion of Innovation in Service Organizations model, thus providing a theoretical foundation to support them. Various positive outcomes were reported for patients, providers and navigators, as well as the health and social care system, although they need to be considered with caution since the majority of studies were descriptive. CONCLUSIONS: This study contributes new knowledge that can inform the initiation and maintenance of primary care patient navigation programs that link patients with CBHSS. It also provides directions for future research.
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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 | 0.034 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".