Building an Evidence-Based Framework for the Development of a Newfoundland and Labrador Centre on Aging
Bibliographic record
Abstract
Life expectancies are increasing and populations are aging in all western industrialized countries. Given the demographics, the health and well-being of older adults and the provision of services for seniors are major priorities for all levels of government and will become increasingly so. Newfoundland and Labrador (NL) has one of the highest proportions (13.9%) of seniors of any province and that percentage is expected to increase to 20% by 2017 (Provincial Healthy Aging Policy Framework, 2007). Statistics Canada has recently projected that by 2031, NL will have the highest proportion of older adults in Canada. The Government of Newfoundland and Labrador has acknowledged the need to address the challenges of an aging population in the Healthy Aging Policy Framework. There are approximately 18 Canadian research centres involved in the study of aging. The only province without a centre dedicated to the study of aging is NL1. The establishment of a NL Centre on Aging would be an important development given the demographics and the unique circumstances of the province and its people. A NL Centre on Aging would facilitate aging-related research and education, thereby assisting the government of NL in achieving the goals of the Healthy Aging Policy Framework. The present document describes a qualitative descriptive investigation funded by the Healthy Aging Research Program (HARP) of the NL Centre for Applied Health Research (NLCAHR) and by the Grenfell Campus of Memorial University.
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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 | 0.494 | 0.399 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.043 | 0.031 |
| Open science | 0.017 | 0.035 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".