Pain language and gender differences when describing a past pain event
Bibliographic record
Abstract
Pain is a largely subjective experience, and one which is difficult to convey to others, and relies significantly on language to be communicated. The language used to describe pain is therefore an important aspect of understanding and assessing another's pain. A growing body of research has reported differences in the pain experienced by men and women. However, few studies have examined gender differences, where gender is understood in both the biological and the social sense, in the language used when reporting pain. The purpose of this descriptive and analytical study was to explore gender differences in the language used by articulate men and women when describing a recollected painful event. Two-hundred and one students from an Australian university (35.32% males and 64.68% females) provided written descriptions of a past pain event. These descriptions were analysed using content analysis. Gender differences were identified in the words and patterns of language used, the focus of pain descriptions, and the reported emotional response to pain. Women were found to use more words (t=4.87, p<0.001), more McGill Pain Questionnaire descriptors (chi(2)=3.07, p<0.05), more graphic language than men, and typically focused on the sensory aspects of their pain event. Men used fewer words, less descriptive language, and focused on events and emotions. Common themes were the functional limitations caused by pain, difficulty in describing pain, and the dual nature of pain. Clinical implications include the value of gathering free pain descriptions as part of assessment, and the use of written pain descriptions.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".