A population-based approach to integrated healthcare delivery: a scoping review of clinical care and public health collaboration
Bibliographic record
Abstract
BACKGROUND: A population-based approach to healthcare goes beyond the traditional biomedical model and addresses the importance of cross-sectoral collaboration in promoting health of communities. By establishing partnerships across primary care (PC) and public health (PH) sectors in particular, healthcare organizations can address local health needs of populations and improve health outcomes. The purpose of this study was to map a series of interventions from the empirical literature that facilitate PC-PH collaboration and develop a resource for healthcare organizations to self-evaluate their clinical practices and identify opportunities for collaboration with PH. METHODS: A scoping review was designed and studies from relevant peer-reviewed literature and reports between 1990 and 2017 were included if they met the following criteria: empirical study methodology (quantitative, qualitative, or mixed methods), based in US, Canada, Western Europe, Australia or New Zealand, describing an intervention involving PC-PH collaboration, and reporting on structures, processes, outcomes or markers of a PC-PH collaboration intervention. RESULTS: Out of 2962 reviewed articles, 45 studies with interventions leading to collaboration were classified into the following four synergy groups developed by Lasker's Committee on Medicine and Public Health: Coordinating healthcare services (n = 13); Applying a population perspective to clinical practice (n = 21); Identifying and addressing community health problems (n = 19), and Strengthening health promotion and health protection (n = 21). Furthermore, select empirical examples of interventions and their key features were highlighted to illustrate various approaches to implementing collaboration interventions in the field. CONCLUSIONS: The findings of our review can be utilized by a range of organizations in healthcare settings across the included countries. Furthermore, we developed a self-evaluation tool that can serve as a resource for clinical practices to identify opportunities for cross-sectoral collaboration and develop a range of interventions to address unmet health needs in communities; however, the generalizability of the findings depends on the evaluations conducted in individual studies in our review. From a health equity perspective, our findings also highlight interventions from the empirical literature that address inequities in care by targeting underserved, high-risk populations groups. Further research is needed to develop outcome measures for successful collaboration and determine which interventions are sustainable in the long term.
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 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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".