A new roadmap for an age-inclusive workforce management practice and an international policies comparison
Bibliographic record
Abstract
Background Worldwide, the worker population age is growing at an increasing rate. Consequently, government institutions and companies are being tasked to find new ways to address age-related workforce management challenges and opportunities. The development of age-friendly working environments to enhance ageing workforce inclusion and diversity has become a current management and national policy imperative. Since an ageing workforce population is a spreading worldwide trend, an identification and analysis of worker age related best practices across different countries would help the development of novel palliative paradigms and initiatives. Methods This study proposes a new systematic research-based roadmap that aims to support executives and administrators in implementing an age-inclusive workforce management program. The roadmap integrates and builds on published literature, best practices, and international policies and initiatives that were identified, collected, and analysed by the authors. The roadmap provides a critical comparison of age-inclusive management practices and policies at three different levels of intervention: international, country, and company. Data collection and analysis was conducted simultaneously across eight countries: Canada, France, Germany, Italy, Japan, New Zealand, Slovenia, and the USA. Results and conclusions The findings of this research guide the development of a framework and roadmap to help manage the challenges and opportunities of an ageing workforce in moving towards a more sustainable, inclusive, and resilient labour force.
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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 | 0.123 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".