A linguistic analysis of female and male opening posts on an online forum dedicated to pain
Bibliographic record
Abstract
Previous research has highlighted differences in the way that men and women talk about pain and in the extent to which word choices correspond with language-based diagnostic tools for pain, specifically, the McGill Pain Questionnaire (MPQ). In this study, we apply procedures from Corpus Linguistics, in which computer software assists in identifying statistically significant patterns in language use, to explore 8697 Opening posts to an online forum dedicated to pain. We determine the extent to which descriptions of pain in the forum include terms that appear in the MPQ and we consider female contributions and male contributions to investigate how reports of pain and its effects relate to gender. Our findings show that there is a large set of vocabulary that is used by both female and male contributors in relation to various aspects of pain experiences. In addition, female contributors to the forum use a wider variety of terms in reference to the quality, intensity, duration and regularity of pain, including a larger number of terms that appear in the MPQ. In sum, female contributors use a wider range of terms in relation to pain and differences in the contexts in which female and male contributors discuss the impacts of pain correspond with gender tropes. Understanding the impacts of pain on individuals’ social lives and recognising how this and the articulation of pain experiences is informed by gender conventions can help health professionals to respond effectively. • We can investigate reported sex/gender differences in reports of pain through online forum data. • There is a common vocabulary used by female and male contributors. • Female participants use a wider variety of terms in relation to pain. • Many terms of the McGill Pain Questionnaire are not used by forum members. • Female and male contributors refer to different contexts when discussing the impacts of pain.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".