Ageism and Sexism: The Double Jeopardy Affecting Older Women's Mental Health
Bibliographic record
Abstract
Older women face unique psychological challenges that stem from the intersection of ageism and sexism, phenomena that together pose a double jeopardy to their mental health and overall well-being. This article explores the implications of these biases, the social and personal impacts they engender, and the need for a more supportive societal framework. The intersection of ageism and sexism creates a complex landscape of challenges that significantly affect the mental health and quality of life of older women. This double jeopardy not only impacts their personal and professional lives but also limits their visibility and representation in society. As research continues to shed light on these issues, it is crucial for policymakers, practitioners, and society at large to develop strategies that address these biases comprehensively. This involves creating supportive workplace environments, providing accurate and empowering health information, and fostering a culture that celebrates rather than stigmatizes aging and femininity. Only through a concerted effort can we hope to dismantle the barriers that older women face, paving the way for a more inclusive and equitable society.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".