A multiple case study of intersectoral public health networks: experiences and benefits of using research
Bibliographic record
Abstract
BACKGROUND: Network partnerships between public health and third sector organisations are being used to address the complexities of population level social determinants of health and health equity. An understanding of how these networks use research and knowledge is crucial to effective network design and outcome evaluation. There is, however, a gap in the literature regarding how public health networks use research and knowledge. The purpose of this paper is to report on the qualitative findings from a larger study that explored (1) the experiences of public health networks with using research and knowledge, and (2) the perceived benefits of using research and knowledge. METHODS: A multiple case study approach framed this study. Focus group data were collected from participants through a purposive sample of four public health networks. Data were analyzed using Framework Analysis and Nvivo software supported data management. Each network had the opportunity to participate in data interpretation. RESULTS: All networks used published research studies and other types of knowledge to accomplish their work, although in each network research and knowledge played different but complementary roles. Neither research nor other types of knowledge were privileged, and an approach that blended varied knowledge types was typically used. Network experiences with research and knowledge produced individual and collective benefits. A novel finding was that research and knowledge were both important in shaping network function. CONCLUSIONS: This study shifts the focus in the current literature from public health departments to the community setting where public health collaborates with a broader spectrum of actors to ameliorate health inequities. Both formal research and informal knowledge were found to be important for collaborative public health networks. Examining the benefits of research and knowledge use within public health networks may help us to better understand the relationships among process (the collaborative use of research and knowledge), structure (networks) and outcomes (benefits).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.038 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".