Codevelopment and usability testing of Patient Engagement 101: a Patient-Oriented Research Curriculum in Child Health e-learning module for health care professionals, researchers and trainees
Bibliographic record
Abstract
BACKGROUND: Patient and family engagement is thought to improve the quality and relevance of child health research. We developed and evaluated the usability of Patient Engagement 101, an e-learning module designed to strengthen the patient-oriented research readiness of health care professionals, researchers, trainees and other stakeholders. METHODS: The development of Patient Engagement 101 was co-led by a parent and a researcher and overseen by a diverse multistake-holder steering committee. The module was refined and evaluated using a mixed-methods usability testing approach with 2 iterative cycles of semistructured interviews, observations and questionnaires. We collected module feedback by way of semistructured interviews, the validated System Usability Scale, and satisfaction, knowledge and confidence questionnaires. Thematic coding of transcripts and field notes, informed by team discussions, guided the module revisions. RESULTS: Thirty end-users completed usability testing (15 per cycle). In each cycle, we modified the module with respect to its content, learner experience, learner-centred design and aesthetic design. Participants were highly satisfied, and System Usability Scale scores indicated the module had the best imaginable usability. Substantial increases in the participants' knowledge test scores and the confidence to engage in patient-oriented research, but not self-rated knowledge, were observed after module completion. INTERPRETATION: Codevelopment with patients and caregivers, and refinement through comprehensive end-user testing, resulted in a training resource with exceptional usability that improved knowledge and confidence to engage in patient-oriented research in child health. Patient Engagement 101 is openly available online, and the methods used to develop and evaluate it may facilitate the creation and evaluation of similar capacity-building resources.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".