Gender Disparities in End of Life Care: A Scoping Review
Bibliographic record
Abstract
Objective: Traditional gender norms and expectations may disproportionately constrain in-home palliative care received by women. This scoping review aims to canvass and evaluate the literature on gender disparities in end of life care and explore relevant themes that could inform future research and practice. Methods: A systematic search of MEDLINE, OVID, COCHRANE, and EMBASE was conducted using MeSH terms palliative care, palliative medicine, terminal care, or hospice care, combined with gender equity, sex factors, sexism, or gender disparities. Articles were limited to those in English (2010 to 2021), focusing on end of life care, gender roles, patients, and caregivers. Results: Of 624 articles identified, 15 met inclusion criteria for critical appraisal using the AMSTAR checklist for systematic reviews and NICE guidelines for quantitative and qualitative studies. Most studies were of poor to moderate quality. Thematic analyses identified 6 major themes related to gender disparities: living situation, symptom experience, care context, care preferences, caregiving, and coping strategies. Conclusion: Larger scale research of better quality is needed to fully characterize gender disparities in end of life care and understand how physicians might mitigate these disparities by building awareness of personal gender biases, providing support to families, educating them, and initiating care discussions that overturn traditional and stereotypic gendered expectations.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".