The Role of Partnership in The Integration of Intersectoral Data
Bibliographic record
Abstract
IntroductionThe importance of the determinants of health to health outcomes has long been established. Historically, data from each of these sectors has been captured in disparate, often siloed, sources. Attempts to integrate these data have faced a number of challenges including technical, legislative and interpretative barriers, creating inefficiencies and inhibiting knowledge sharing. Despite this, there have been notable successes where intersectoral data and health data have been brought together in a meaningful way. The establishment of strong partnerships, with academia, governments, privacy and legal sectors, and other bodies, across sectors has been key to this success. These partnerships ensure data are integrated, analyzed, and interpreted accurately and appropriately, while also leveraging existing investments and expertise.
 Objectives and ApproachThe objective of this session is to explore the role of partnerships throughout the data integration life cycle, from initial discussions, to data integration, through to connecting research output to policy impact. Each of the presenters will discuss the successes, barriers and mitigation strategies they have experienced across different jurisdictions using real world examples.
 ResultsHealth research institutes globally are increasingly able to access routinely collected intersectoral data from non-health sectors. In each institute, data are unique, complex and have been collected in a manner consistent with the needs of the sector. As health research institutes work to understand the data structures and determine the best way to link, use and interpret the information according to national and international best practice guidelines, it has become clear that it is critical to undertake this in partnership with experts from each sector, who understand how the data was collected and can guide appropriate interpretation. In addition, these partnerships have enabled the connection of policy priorities in other sectors with research done in the health sector using intersectoral data. For example, in addition to supporting government health departments, health research institutes have collaborated with other government ministries including immigration, social services, and education. This session will present real world examples from local (provincial), national and international contexts, and highlight a novel data platform, being developed to minimize barriers to data access and use across sectors and jurisdictions.
 Conclusion / ImplicationsThe participants on this panel will demonstrate the importance of partnership throughout the data integration life cycle when working with intersectoral data using real world examples. Collaboration increases the value of integrated data to both health and non-health sectors, through the connection of policy priorities and support of research across the determinants of health.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".