Health Care System Collaboration to Address Chronic Diseases: A Nationwide Snapshot From State Public Health Practitioners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Until recently, health care systems in the United States often lacked a unified approach to prevent and manage chronic disease. Recent efforts have been made to close this gap through various calls for increased collaboration between public health and health care systems to better coordinate provision of services and programs. Currently, the extent to which the public health workforce has responded is relatively unknown. The objective of this study is to explore health care system collaboration efforts and activities among a population-based sample of state public health practitioners. METHODS: During spring 2013, a national survey was administered to state-level chronic disease public health practitioners. Respondents were asked to indicate whether or not they collaborate with health care systems. Those who reported "yes" were asked to indicate all topic areas in which they collaborate and provide qualitative examples of their collaborative work. RESULTS: A total of 759 respondents (84%) reported collaboration. Common topics of collaboration activities were tobacco, cardiovascular health, and cancer screening. More client-oriented interventions than system-wide interventions were found in the qualitative examples provided. Respondents who collaborated were also more likely to use the Community Guide, use evidence-based decision making, and work in program areas that involved secondary, rather than primary, prevention. CONCLUSION: The study findings indicate a need for greater guidance on collaboration efforts that involve system-wide and cross-system interventions. Tools such as the Community Guide and evidence-based training courses may be useful in providing such guidance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it