Co-development and Usability Testing of Research 101: A Patient-Oriented Research Curriculum in Child Health (PORCCH) E-Learning Module for Patients and Families
Bibliographic record
Abstract
Background Engaging patients and families as research partners increases the relevance, quality, and impact of child health research. However, those interested in research engagement may feel underequipped to meaningfully partner. We sought to co-develop an online learning (e-learning) module, “Research 101,” to support capacity-development in patient-oriented child health research amongst patients and families. Methods Module co-development was co-led by a parent and researcher, with guidance from a diverse, multi-stakeholder steering committee. A mixed-methods usability testing approach, with three iterative cycles of semi-structured interviews, observations, and questionnaires, was used to refine and evaluate the e-learning module. Module feedback was collected during testing and a post-module interview, and with the validated System Usability Scale (SUS), and satisfaction, knowledge, and self-efficacy questionnaires. Transcripts and field notes were analyzed through team discussion and thematic coding to inform module revisions. Results Thirty participants fully tested Research 101, and another 15 completed confirmatory usability testing (32 caregivers, 6 patients, and 7 clinician-researchers). Module modifications pertaining to learner-centered design, content, aesthetic design, and learner experience were made in each cycle. SUS scores indicated the overall usability of the final version was “excellent.” Participants' knowledge of patient-oriented research and self-efficacy to engage in research improved significantly after completing Research 101 (p < 0.01). Conclusions Co-development and usability testing facilitated the creation of an engaging and effective resource to support the scaling up of patient-oriented child health research capacity. The methods and findings of this study may help guide the integration of co-development and usability testing in creating similar 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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".