Patients and students co‐develop a resource database
Bibliographic record
Abstract
BACKGROUND: Health professional students are provided with a wealth of online learning resources recommended by curriculum developers or instructors, the majority of which focus on biological and clinical science. Our goal was to develop a database of learning resources to help students and faculty members understand chronic health conditions from a patient's perspective. Resources were recommended by patients and evaluated by students. Our goal was to develop a database of learning resources … recommended by patients and evaluated by students METHODS: Patients and caregivers who recommend resources to their students in an interprofessional health mentors programme, and participants in a Disability Learning Resource planning session, provided 68 different resources, ranging from community organisation websites to personal biographies. Resources were organised into eight categories and rated by 10 senior health professional students. Patients … provided 68 different resources, ranging from community organisation websites to personal biographies RESULTS: Patients recommended resources so that students could learn what it is like to live with a particular condition, and also learn about useful patient information resources and community-based advocacy organisations. Students identified 40% of the rated resources as useful or exceptionally useful, and identified the characteristics of useful and not useful resources. Students identified 40% of the rated resources as useful or exceptionally useful … CONCLUSIONS: Students want resources that are easy to navigate and are well organised. They want a 'one-stop shop' to access information about a particular condition or disease, and value resources that they can recommend to their patients as well as use to expand their own knowledge. Students value information about local organisations for specific conditions that they can connect their patients to, and from which they may learn more about existing support initiatives in their communities. Clinical educators could better prepare students for practice by making available patient-recommended resources. Students … value resources that they can recommend to their patients as well as use to expand their own knowledge Students value information about local organisations for specific conditions that they can connect their patients to … Clinical educators could better prepare students for practice by making available patient-recommended 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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.029 |
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".