Employers’ Support of Older Adults Facing Ageism in the Workplace: A Scoping Review of the Literature
Bibliographic record
Abstract
Abstract As the Canadian population continues to age rapidly, addressing the social structures that negatively impact older adults is of increasing importance. The most prominent of these social structures is the workplace, which can be a potential source of age discrimination. The goal of this scoping review was to analyze the literature that addresses strategies for employers to support older workers experiencing ageism, in order to answer the research question: How can employers support older adults (50+) facing ageism in the workplace? Following Arksey and O’Malley’s five-step framework, an electronic database and grey literature search was conducted between September and December 2020. Thematic content analysis was performed to establish key themes. The search revealed 3,635 peer-reviewed and grey literature sources that were evaluated by three investigators. Thirty-six articles, published between 2006 and 2020, met inclusion criteria and examined various support strategies for employers. Five major emerging themes were identified from the literature: (1) Recruitment practices, (2) Training opportunities, (3) Education for managers, leaders, and employees in the workplace, (4) Flexible employment opportunities, and (5) Methods to change the psycho-social environment of the workplace. Implementation of these interventions is required to support older adults who may be experiencing workplace ageism. Longitudinal research of these interventions is required to determine the lasting effects of these strategies; however, the existing literature supports the implementation of these supportive actions, which is vital to ensuring that older adults are able to attain and maintain valuable work, in healthy environments, now and into the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".