Reframing Women’s Retirement Adjustment: A Systematic Review Through a Resource-Based Perspective
Bibliographic record
Abstract
This study presents a systematic scoping review of women’s retirement adjustment through the lens of the Retirement Resources Inventory framework (Wang et al., 2011). Despite women’s increasing workforce participation since World War II, their unique retirement experiences remain underexplored and often analyzed through male-centric models. By examining 162 peer-reviewed studies, this review identifies key antecedents influencing women’s resource accumulation and adjustment quality: (1) pre-retirement occupational status, (2) control over retirement decisions and proactive planning, (3) psychological disposition and values, and (4) post-retirement social engagement. Findings reveal that women face unique challenges—such as caregiving responsibilities, career interruptions, and the gender pay gap—that shape their retirement journeys. The study highlights significant gaps, including the need for longitudinal research, intersectional analyses, and regionally diverse studies to fully capture women’s experiences. The review contributes to Career Succession and Human Resource Management (HRM) by advocating for gender-sensitive retirement frameworks and tailored interventions to support women’s emotional, social, and financial well-being. By addressing these gaps, scholars and practitioners can create inclusive policies, facilitate knowledge transfer, and advance gender equity in retirement practices to better align with women’s evolving roles in the workforce and 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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".