Assessing medical student learning in assessing the quality ofhealth information on the internet and communicating the skill topatients
Bibliographic record
Abstract
BACKGROUND: Patients increasingly turn to the internet for health information. However, seeking valid information can be difficult because of the speed of accumulation of information and lack of control. HealthInSite, the Canadian Health Network, the Health On the Net Foundation and the QUality Information ChecKlist have created criteria to assess internet health information. The fourth semester students at the Manipal College of Medical Sciences, Pokhara, Nepal, were taught to assess health information online and to communicate the same to simulated patients. Student feedback regarding the exercise was collected using a questionnaire. METHODS: The exercise was carried out during the pharmacology practical sessions in small groups of seven or eight students each. The students developed their own checklist using information from the organisation websites mentioned above. Each group analysed a particular health website. During the second session the groups communicated the critical appraisal criteria to a simulated patient. Then the patient chose websites for a particular disease condition. Formative assessment of the sessions was carried out. A questionnaire was used to collect student feedback about the sessions. Basic demographic information was collected. Student attitude was studied by noting their degree of agreement with a set of seven statements using a Likert-type scale. The median score was calculated. RESULTS: A total of 56 of the 73 fourth semester students participated. The gender ratio was equal. The common nationalities were Indians, Nepalese and Sri Lankans. The median score was 27 (maximum score 35) and the interquartile range was 4. There were no significant differences in the total scores among different subgroups of respondents. The students wanted similar sessions to be frequently incorporated during the course. Formative assessment revealed that the groups worked cohesively. They were able to analyse the given website appropriately and were successful in communicating the assessment criteria to the simulated patient. CONCLUSIONS: The sessions should be continued and strengthened and could be expanded to other semesters and especially to students during the clinical years of study. Preliminary feedback was positive but more detailed studies are required.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".