The Angle Labor Pain Questionnaire
Bibliographic record
Abstract
OBJECTIVES: The Angle Labor Pain Questionnaire (A-LPQ) is a new, condition-specific, multidimensional psychometric instrument that measures the most important dimensions of women's childbirth pain experiences using 5 subscales: The Enormity of the Pain, Fear/Anxiety, Uterine Contraction Pain, Birthing Pain, and Back Pain/Long Haul. This study assessed the A-LPQ's test-retest reliability during early active labor without pain relief. METHODS: Two versions of the A-LPQ were randomly administered to laboring women during 2 test sessions separated by a 20-minute window. Participants were of mixed parity, contracting ≥3 minutes apart, cervical dilation ≤6 cm, and without pain relief. Changes in pain were rated using the Patient Global Impression of Change Scale. Overall pain intensity and pain coping were rated using the Numeric Rating Scale (NRS) and the Verbal Rating Scale (VRS) and the Pain Mastery Scale (PMS) respectively. A-LPQ test-retest reliability (primary outcome), Cronbach's α, and concurrent validity with NRS, VRS, and PMS scores were assessed (n=104). Responsiveness was assessed in 55 women who reported changes in pain. RESULTS: A-LPQ summary and subscale scores demonstrated good test-retest reliability (ICCs, 0.96 to 0.89), trivial to moderate sensitivity to change, and high responsiveness to minimal changes in pain (0.85 to 1.50). Cronbach's α for A-LPQ summary scores was excellent (0.94) and ranged from 0.72 to 0.94 for subscales. Concurrent validity was supported by moderate to strong correlations with NRS and VRS scores for overall pain intensity and PMS scores for pain coping. DISCUSSION: Findings support A-LPQ use for assessing women's childbirth pain experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".