Barriers to Medication Decision Making in Women with Lupus Nephritis: A Formative Study using Nominal Group Technique
Bibliographic record
Abstract
OBJECTIVE: To assess the perspectives of women with lupus nephritis on barriers to medication decision making. METHODS: We used the nominal group technique (NGT), a structured process to elicit ideas from participants, for a formative assessment. Eight NGT meetings were conducted in English and moderated by an expert NGT researcher at 2 medical centers. Participants responded to the question: "What sorts of things make it hard for people to decide to take the medicines that doctors prescribe for treating their lupus kidney disease?" Patients nominated, discussed, and prioritized barriers to decisional processes involving medications for treating lupus nephritis. RESULTS: Fifty-one women with lupus nephritis with a mean age of 40.6 ± 13.3 years and disease duration of 11.8 ± 8.3 years participated in 8 NGT meetings: 26 African Americans (4 panels), 13 Hispanics (2 panels), and 12 whites (2 panels). Of the participants, 36.5% had obtained at least a college degree and 55.8% needed some help in reading health materials. Of the 248 responses generated (range 19-37 responses/panel), 100 responses (40%) were perceived by patients as having relatively greater importance than other barriers in their own decision-making processes. The most salient perceived barriers, as indicated by percent-weighted votes assigned, were known/anticipated side effects (15.6%), medication expense/ability to afford medications (8.2%), and the fear that the medication could cause other diseases (7.8%). CONCLUSION: Women with lupus nephritis identified specific barriers to decisions related to medications. Information relevant to known/anticipated medication side effects and medication cost will form the basis of a patient guide for women with systemic lupus erythematosus, currently under development.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".