Can programmed aging theory adequately explain sexual dysfunction among the elderly?
Bibliographic record
Abstract
The belief is widespread that elderly individuals simply become asexual as they age. Consequently, many caregivers and healthcare practitioners implicitly ignore or downplay the sexuality and sexual needs of the elderly. Although it is true that elderly individuals commonly experience sexual dysfunction, which may include a decline in sexual desire, sexual functioning, and ability to engage in sexual activity, most desire to remain sexually active into their older age, and many in fact do. This review examines the extent to which programmed aging theory, which holds that senescence and its associated physiological decline result from genetically predetermined lifespan, can be used to explain and evaluate the development of sexual dysfunction among elderly individuals. Although programmed aging theory usefully accounts for and normalizes inevitable changes in sexual function and ability, it ignores the psychological and psychosocial aspects of aging that affect the onset and extent of sexual dysfunction. Acknowledging these aspects of aging has led to interventions which have proved helpful in maintaining and enhancing sexual activity and wellbeing among the elderly. As the population ages and average lifespans increase, it is necessary that caregivers and healthcare practitioners are equipped to help their patients understand, manage, and adapt to age-related changes in sexual desire, functioning, and wellbeing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".