Gender differences in the quantitative and qualitative assessment of chronic pain among older people
Bibliographic record
Abstract
Background Pain, regardless of its causes, is a subjective and multidimensional experience that consists of sensory, emotional and cognitive factors that cannot be adequately captured by a single number on a pain scale. The aim of the study was to understand gender differences in the assessment of quantitative and qualitative chronic pain among older people. Methods The study used a questionnaire that included questions about demographic and social characteristics as well as the following scales: Abbreviated Mental Score (AMTS), Personal Activities of Daily Living (PADL) by Katz, Instrumental Activities of Daily Living (IADL) by Lawton, Geriatric Depression Scale (GDS-15), McGill Pain Questionnaire (MPQ). Results The pain rating index based on rank values of adjectives was higher among women than men (18.36 ± 7.81 vs. 17.17 ± 9.69, p = 0.04). The analysis of the frequency of selection of individual adjectives describing the sensory aspects of pain showed that men described the pain as “stabbing” more often than women (26.1% vs. 14.3%, p < 0.05). Women chose adjectives from the emotional category more often than men (59.8% vs. 75.4%, p < 0.05), describing the pain as “disgusting” (8.9% vs. 1.4%, p < 0.05), “unbearable” (19.6 vs. 4.3, p < 0.05). In the subjective category, there was a difference between women and men in terms of describing pain as “terrible” (23.2% vs. 7.2%, p < 0.05) and as “unpleasant” (11.6% vs. 23.3%, p < 0 0.05). Conclusion When referring to pain, women tend to employ more detailed and factual language, indicative of heightened emotional sensitivity. Men tend to use fewer words and focus on the sensory aspects of pain. Subjective aspects of pain were demonstrated by both women and men.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".