Reliability of a Health Questionnaire Among Women With Brain Injury
Bibliographic record
Abstract
Research with respect to traumatic brain injury (TBI) has identified differential health outcomes by gender. However, despite this, a lack of research exists regarding the impact of TBI on specific health-related issues for women. This study investigated rater agreement of a health questionnaire developed to identify health concerns among women with TBI. Thirteen women with moderate to severe brain injury completed the questionnaire twice (1 week apart) and provided feedback regarding content and ease of use. The questionnaire measures areas of menses, conception, menopause, thyroid function, and change in participation in activities after brain injury. Kappa coefficients and percentage agreement were generated to determine levels of agreement of participant responses between the two sessions. Kappa coefficients ranged from .21 to 1.00, with the highest agreement for items requiring concrete responses (e.g., yes/no) and the lowest with items requiring more subjective answers. Percentage agreement ranged from 58% to 100%. Participants rated the utility of the questionnaire as high (73%-100%). This questionnaire proved to be a reliable and useful method to gather information regarding health in women with TBI. By identifying health issues early, diagnostic and treatment intervention may be delivered in a timelier and effective manner. Further psychometric testing is required to ensure the validity of responses.
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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 | 0.025 | 0.051 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".