The development of national indicators for the surveillance of osteoporosis in Canada
Bibliographic record
Abstract
INTRODUCTION: The Public Health Agency of Canada, in collaboration with bone health and osteoporosis experts from across Canada (n = 12), selected a core set of indicators for the public health surveillance of osteoporosis using a formal consensus process. METHODS: A literature review identified candidate indicators that were subsequently categorized into an osteoporosis-specific indicator framework. A survey was then administered to obtain expert opinion on the indicators' public health importance. Indicators that scored less than 3 on a Likert scale of 1 (low) to 5 (high) were excluded from further consideration. Subsequently, a majority vote on the remaining indicators' level of public health importance was sought during a face-to-face meeting. RESULTS: The literature yielded 111 indicators, and 88 were selected for further consideration via the survey. At the face-to-face meeting, more than half the experts considered 39 indicators to be important from the public health perspective. CONCLUSION: This core set of indicators will serve to inform the development of new data sources and the integration, analysis and interpretation of existing data into surveillance products for the purpose of public health action.
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 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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".