Can older workers be retrained? Canadian evidence from worker-firm linked data
Bibliographic record
Abstract
Our empirical analysis is based on Statistics Canada’s worker-firm matched data set, the 2003 Workplace and Employee Survey (WES). The sample size is substantial: about 4,000 workers over the age of 50 and 12,000 between the ages of 25 and 49. Training was a focus of the survey, which offers a wealth of worker-related and firm-related training variables. We found that the mean probability of receiving training was 9.3 percentage points higher for younger workers than for older ones. Almost half of the gap is explained by older workers having fewer training-associated characteristics (personal, employment, workplace, human resource practices and occupation/industry/region), and slightly more than half by them having a lower propensity to receive training, this being the gap that remained after we controlled for differences in training-associated characteristics. Their lower propensity to receive training likely reflects the higher opportunity cost of lost wages during the time spent in training, possible higher psychological costs and lower expected benefits due to their shorter remaining work-life and lower productivity gains from training, as discussed in the literature. The lower propensity of older workers to receive training tended to prevail across 54 different training measures, with notable exceptions discussed in detail. We found that older workers can be trained, but their training should be redesigned in several ways: by making instruction slower and self-paced; by assigning hands-on practical exercises; by providing modular training components to be taken in stages; by familiarizing the trainees with new equipment; and by minimizing required reading and amount of material covered. The concept of “one-size-fits- all” does not apply to the design and implementation of training programs for older workers.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".