Validation of the Patient Activation Measure in kidney stone disease patients
Bibliographic record
Abstract
INTRODUCTION: We aimed to validate the Patient Activation Measure (PAM) within a kidney stone disease (KSD) population, determine the variability of patient activation within this population, and characterize relationships between activation and variables such as health literacy, quality of life, and demographics. METHODS: This cross-sectional study includes individuals 18 years or older followed for KSD at University of Montreal Hospital Center. Demographic data and responses for the PAM, Wisconsin Stone Quality of Life scale, and Health Literacy Questionnaire (HLQ) were acquired. RESULTS: Females and those with poor medication adherence were found to have significantly lower activation. The HLQ dimensions "Actively managing my health," "Navigating the healthcare system," and "Understand health information well enough to know what to do" were associated with significantly higher activation. Rasch analysis revealed an item reliability of 0.81, a person reliability of 0.98, and a Cronbach's alpha of 0.88. Regarding item fit, only item 1 (When all is said and done, I am the person who is responsible for taking care of my health) fit poorly with the model. Principle component analysis revealed evidence of a second dimension, accounting for 9.0% of the variation in observed responses. CONCLUSIONS: Female sex and poor medication adherence were associated with significantly lower activation. Aspects of health literacy concurring with the precise definition of "activation" were associated with significantly higher PAM scores. The PAM was found to have good person and item reliability, and good internal consistency; however, principal component analysis revealed that construct validity is possibly threatened by multidimensionality.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".