The BC SUPPORT Unit Data Platform: Offering Data-Related Services To Researchers In British Columbia
Bibliographic record
Abstract
IntroductionThe Canadian Institutes of Health Research (CIHR) and provinces co-fund local Units to increase the quality and quantity of patient-oriented research. These SUPPORT (Support for People and Patient-Oriented Research and Trials) Units include a prominent Data Plan component. The BC Plan is the result of collaboration between many organizational partners. Objectives and ApproachA Data Advisory Committee comprised of eight organizational partners worked together for several months in 2016-2017 to develop BC’s provincial Data Plan. The Data Plan includes seven objectives; in general, the plan seeks to make additional data available for research, increase the speed and transparency of data access, and offer services to enable more efficient data use. The services resulting from the Data Plan are intended to improve support for the entire continuum of a research project, from developing a research question to analyzing the results. Several projects are part of Ministry of Health-led work developing a Health Data Platform. ResultsThe projects initiated so far as part of the Data Plan include: BC Data Scout\textsuperscript{TM}: an online tool that provides aggregate cohort information to inform research question development; REDCap: software to support privacy-sensitive data collection and management; INFORM: software to support data collection for complex clinical research studies and trials; Direct Access: enables Population Data BC to access BC Ministry of Health databases so researchers have access to up-to-date data; Streamlining: making the data request process more efficient; New datasets: several projects that will provide new data sources, including patient experience and outcome measures and secondary use data drawn from electronic medical records; and Inventory: an online catalog for all high-value and linkable data sets available to researchers. Conclusion/ImplicationsThe services and tools included in BC’s Data Plan will help researchers develop and deliver world-class research and inform important health care decisions. The patient-oriented focus of these services help to ensure that research is done in partnership with patients and centered on research questions that matter to them.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.249 | 0.098 |
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".