Knowledge assessment tools in atopic dermatitis patient education: a scoping review
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a chronic and inflammatory skin disease which requires continuous self-management by patients and caregivers. Patient education in AD can improve the self-management practices, treatment adherence rates, and clinical outcomes of patients. Patient-reported outcome measures and objective clinical outcome measures have been used to assess the effectiveness of AD patient education interventions, however they have limited use in assessing learning outcomes, such as knowledge. The literature on knowledge outcome measures for AD patient education interventions has not been examined to date. MAIN: We performed a scoping review of the literature on knowledge assessment tools for AD patient education interventions following the PRISMA-ScR framework. Search databases included MEDLINE, Embase, CINAHL, Education Source, Web of Science, Grey Matters, Clinical Trials.gov, and the International Clinical Trials Registry Platform (ICTRP). Of the 3914 articles identified from the search strategy, 20 studies were eligible for data extraction and summarized by narrative synthesis. Most studies were randomised controlled trials originating in the United States, Europe, and Asia, and published in the years of 2003-2023. Researchers commonly evaluated caregivers' knowledge of AD and included assessments of clinical outcome measures. Similar methods were employed for assessing subjective knowledge across studies. Likewise, studies measuring AD patient/caregiver objective knowledge used comparable methods. Multiple-choice and true/false question formats were used in objective knowledge assessments, and Likert-type scales were common for evaluating subjective knowledge. Objective knowledge assessments consisted of more questions than subjective knowledge outcome measures. Content assessed in knowledge outcome measures was relatively consistent across studies. Delivery of subjective and objective AD knowledge assessments was by telephone, in clinic, and/or online. In pre- and post-test study designs, identical knowledge outcome measures were administered. CONCLUSION: This scoping review highlights the diverse components of knowledge assessment tools for AD patient education interventions. Further studies on developing and validating high-quality AD knowledge outcome measures are needed for assessing the true effects of patient education interventions on improving patient/caregiver knowledge.
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".