Turning Research Ideas into Reality: A Guide to Developing a Simulated Research Protocol using Administrative Data
Bibliographic record
Abstract
Objectives Demonstrate the translation of a research idea into a protocol with an analytic plan, including discussion on requirements for administrative data (AD) analysis, e.g., governance, time estimation. Foster discussion on processes for conducting AD research to enable learning and collaboration. ApproachParticipants were guided through three sections, and each alternated between presentations, group participation and discussion sessions. An overview of the Manitoba Centre for Health Policy (MCHP) Data Repository was presented to use as the framework for the AD discussion. Our chosen topic of discussion was: “How do health history and family history affect risk of poor outcomes for people with diabetes?” In small groups, participants discussed issues arising from translating the idea into a research plan. For example, define a cohort of people with diabetes, examine cardiovascular events as the outcome using survival analysis, and control for demographics, treatment compliance and family history of diabetes and cardiovascular events. We evaluated the feasibility of using AD to measure these variables. We discussed a method to estimate the time to conduct this proposed using AD, i.e., time for programming and analysis. Finally, we discussed data approvals and requirements when conducting research using AD. Conclusions and ImplicationsWe discussed the benefits and cautions of using AD and shared knowledge regarding research opportunities and obstacles across jurisdictions to identify commonalities and areas of opportunity for growth. The workshop was informative and interactive. It generated much discussion and was well-received among participants, with many asking for further information after the workshop concluded.
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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 | 0.177 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.085 | 0.036 |
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".