Pain Assessment Recommendations for Women, Made by Women: A Mixed Methods Study
Bibliographic record
Abstract
Objective: To quantitatively describe women's priorities for pain assessment and qualitatively explain unique features of women's pain experiences. Design: Mixed-methods study that included a three-round Delphi study followed by in-depth interviews. Setting: Clinical research study. Participants: Twenty-three women with chronic pain recruited from three women's pain treatment facilities and one interdisciplinary chronic pain clinic. Methods: Phase 1 (Delphi) involved completion of a questionnaire that rated agreement with the importance of 32 commonly used pain assessment measures. Answers were compiled, and controlled feedback was provided after each round. This iterative process continued until acceptable stability was reached. Stability was defined as proportion agreement for each response that reached the a priori cutoff score of 75%. Phase 2 (qualitative) involved one-to-one telephone interviews that followed a semistructured interview guide partially informed from phase 1 findings. A descriptive approach summarized and described participants' perspectives while avoiding abstractions. Textual data were analyzed using content analysis. Results: Phase 1 identified 15 pain assessments as important. Some commonly used pain assessment measures such as the numeric pain intensity rating scale did not reach agreement as important. However, no pain assessments reached agreement as unimportant. Ten additional women completed face-to-face interviews, and an overall theme of stigmatization emerged that highlighted the importance of soliciting the pain narrative and why some aspects of psychosocial pain assessment did not reach agreement. Conclusions: Priorities identified by women for the assessment of pain were largely consistent with expert recommendations; however, important differences were raised that merit consideration for clinicians to reduce stigma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".