Addressing Fragmentation of Health Services through Data-Driven Knowledge Co-Production within a Boundary Organization
Bibliographic record
Abstract
Behavioral healthcare services involve multiple disconnected sectors and providers serving the same populations. Efforts to identify and address service delivery problems are hampered by fragmentation of datasets. We conducted an engaged research project in which we formed a boundary organization and developed a knowledge co-production process centered on collaborative data sharing and visualization. Multisector participants in [location] worked to access and share data and to collectively interpret the resulting integrated data through visualizations using four knowledge co-production practices: collective interaction with data, perspective taking, reflection & debrief, and iteration of visualizations. The knowledge co-production process was evaluated using qualitative methods. This research extends sociotechnical research on knowledge co-production by proffering collaborative data sharing and visualization as a knowledge co-production process that can extend across disconnected and disparate social groups and contributes to community informatics by highlighting the role boundary organizations can play in facilitating data sharing and data-driven problem solving between fragmented sectors.
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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 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".