Promoting Health Information Literacy to the Wider Community : The Mini-Med School Experience
Bibliographic record
Abstract
1) Program Objective : Collaboration with the University’s Mini-Med School to reach members of the community and to provide support for health information literacy. \n2) Setting : McGill University has offered Mini-Med School, an outreach initiative to educate the public about medical science for the past 5 years. \n3) Participants : Each year, McGill Mini-Med School registers to capacity with over 400 participants. Of these, a small proportion registered for an additional workshop led by a librarian.\n4) Program : While it is widely agreed that information literacy skills should be fostered in institutions of higher education, little is known about attempts to teach about information literacy outside of academia. This year, the Library offered optional, hands-on workshops, on “Finding Health Information Online” to all Mini-Med School participants. A life sciences librarian designed and coordinated the workshops, while consumer health librarians from affiliated hospitals assisted in the delivery. Feedback was obtained from all participants using a paper and pencil questionnaire.\n5) Results : While a modest number of participants chose to take the hands-on workshop, the response to the instruction was overwhelmingly positive.\n6) Conclusion : By collaborating with faculty and staff to deliver health information literacy initiatives to the greater community, librarians have the opportunity to not only reach a broader group of users, but to foster partnerships with researchers in their own institution. The promotion of health information literacy through existing outreach programs such as Mini-Med School is a potential source of increased visibility within and without the university.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".