Is age an issue? Psychosocial differences in perceived older workers’ work (un)adaptability, effectiveness, and workplace age discrimination
Bibliographic record
Abstract
The aging population in the developed world has implied increasing age diversity in the workforce of organizations. Consequently, mutual perceptions about one’s coworkers and age discrimination are becoming increasingly important. This study aims to explore how perceptions about older workers’ work (un)adaptability, work effectiveness, and workplace age discrimination vary according to participants’ psychosocial factors, such as age group, gender, education level, and work sector. This study included a sample of 453 workers in Portugal, diverse in terms of age, gender, education level, and work sector. Four different instruments were used: (a) a sociodemographic questionnaire; (b) an older workers’ Work Adaptability scale; (c) an older workers’ Work Effectiveness Scale and; (d) the Workplace Age Discrimination Scale (WADS). Results: Middle-aged and older participants perceive older workers as more adaptable than younger participants. The oldest group of participants perceives older workers to be more work-effective and experience the highest levels of age discrimination in the workplace, when compared to the other age groups. Also, participants with lower levels of education tend to perceive higher levels of workplace age discrimination, when compared to participants with high school and higher education. Conclusions: Generational perceptions in the workplace are perceived by workers differently, hence organizations should implement age management strategies to address age discrimination, particularly due to the increasing proportion of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".