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Record W2221938488 · doi:10.14236/jhi.v15i3.653

Assessing medical student learning in assessing the quality ofhealth information on the internet and communicating the skill topatients

2007· article· en· W2221938488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Innovation in Health Informatics · 2007
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistThe InternetMedical educationFormative assessmentLikert scaleMedicineSession (web analytics)Quality (philosophy)Information qualityPsychologyFamily medicineInformation systemMathematics educationComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.062
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0620.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.512
Teacher spread0.433 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it