An Electronic Teaching Module for Improving Knowledge of Self-Management of Vaso-Occlusive Pain Crises in Patients With Sickle Cell Disease: Pilot Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: For patients with sickle cell disease (SCD), effective management of vaso-occlusive crises (VOCs) is integral to provision of care, as nearly all affected individuals will suffer from VOCs in their lifetime. A recent systematic review of technological interventions to improve self-management in the care of SCD concluded that electronic health has the potential to improve the care of individuals with SCD. OBJECTIVE: The aim of this study was to assess the value of an electronic teaching module (ETM) provided by Emmi Solutions for educating adult SCD patients on VOC self-management and treatment options for SCD. METHODS: A pretest assessed adults with SCD for baseline knowledge with regard to self-management of VOCs. Participants then watched the 35-min ETM and completed a posttest and survey on the ETM. RESULTS: A total of 20 adults enrolled. Their knowledge scores improved (pretest median 66.5% and posttest median 85%; P<.001). In total, 18 participants (18/20, 90%) agreed that they "learned a lot" or "learned something" from the ETM. The most common topic about which they reported learning was hydroxyurea. A total of 12 participants (12/20, 60%) agreed with the statement that they "would recommend the module to a friend or family member with sickle cell disease." CONCLUSIONS: The ETM is associated with an increase in knowledge in patients with SCD. Limitations of the study include small sample size, no assessment of knowledge before premodule questionnaire completion, and no longitudinal follow-up. Identifying patients with SCD who demonstrate affinity for self-education via an ETM may further enhance utility of this tool to educate and empower patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".