Bibliographic record
Abstract
IntroductionMost public health-related concepts and outcomes can be defined as to their geographic location. The surroundings often have a strong influence or interactions with studied phenomena. For this reason a good understand and accurate geographic placement, linking, and aggregation of studied concepts is a critical yet often underestimated procedure. Objectives and ApproachThe main objectives of this presentation are: 1) an easy to understand review and explanation of geographic delineation markers in common healthcare databases, and 2) ways and pitfalls of geographic data linkages. Common point- and area-defined databases will be described. Nuances of ‘point-to-area’, ‘area-to-area’ linkages will be discussed, with additional explanations of scale and zone effects. Examples of common linkages between the following common spatial delineators will be explained: Postal Code Conversion File (PCCF), small area Canada Census units, common health system geographies (e.g. sub-regions, LHINs). Frequently committed errors and best practices in geographic data linkages will be discussed. ResultsExamples of the influence of various methods of geographic data linkages on study simulated outcomes will be shown. Conclusion/ImplicationsImproper geographic linkage procedures can lead to incorrect study results. Enhancing the knowledge of geographic concepts in public health research and promotion of correct procedures in spatial placements, linkages and aggregation are the main take home messages of this presentation.
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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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.027 |
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".