AMBIVALENT AGEISM IN THE WORKPLACE AND ITS IMPACT: EXPLORING PERCEPTIONS OF OLDER WORKERS
Bibliographic record
Abstract
Abstract Several countries are currently facing significant labour shortages in different work sectors. One of the solutions being considered to deal with such shortages is the retention of older workers. However, to do so, ageist attitudes and discrimination in the workplace must be countered as well as their negative impacts on older workers’ well-being. While previous studies have focused on assessing the impact of hostile ageism in the workplace, less research has been conducted on ambivalent ageism (i.e., stereotypes of fragility and incompetence) in the workplace. This study examines if and to what extent older workers perceive to be the target of ambivalent ageism and how such perceptions impact their well-being, in terms of psychological disengagement, self-esteem, perceived employability as well as intentions to leave their organization. An online, bilingual (French / English) questionnaire was completed by 951 Canadian older workers aged 50 years or more. Preliminary data analysis suggests that ambivalent ageism is negatively associated with perceived employability and self-esteem and positively associated with psychological disengagement and intentions to leave. Further, stratified data analysis by age group suggests that workers aged 62 or older perceive less ambivalent ageism, are less disengaged and have significantly higher self-esteem than workers of younger age groups. Such findings call for the implementation of workplace policies that are age-based inclusive and that account for differential experiences of ageism in the workplace.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".