IAGG WORLD-WIDE REPORTS ON THE ELDER CARE WORKFORCE
Bibliographic record
Abstract
This symposium details the state of the elder care workforce around the world. Reports from IAGG regional chairs indicate that while each region has specific needs and concerns, all uniformly report insufficient numbers of aging experts and the lack of available training to meet current and increasing demands. Our African regional chair, Isabella Aboderin, reports that while Sub Saharan Africa is among the youngest regions in the world, they are experiencing a widespread increase in the number of elders and have few specialized experts or training centers to prepare skilled professional to meet their needs. Latin America is facing a 47% increase in its aging and disabled population according to regional chair, Marianela Hekman. Although there is an impressive increase in the number of professionals interested in aging, Latin America faces a serious deficiency in educational programs targeting aging and looks to IAGG for guidance. North American regional chair, Kenneth Madden, provides insights concerning the aging professional work force in Canada where they have made concerted efforts to increase the number of aging professionals, but struggle with a very uneven distribution of resources. For example, one entire province may have one geriatric specialist while a city in another province has many. Finally, Clemens Tesch-Roemer reports from the European region and elaborates on their struggles with the future of informal and formal care and the education of elder care workforce. In short, each region offers insights into the world-wide struggle to prepare to meet the needs of our aging population.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.034 |
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".