Ensuring access to consumer health information pamphlets at Capital Health
Bibliographic record
Abstract
Program objective – The objective of the program was to create a catalogue of patient education pamphlets and provide a stable in-house platform for the database that is sustainable with current staff resources. Rationale – Capital Health has an excellent selection of more than 1000 pamphlets specifically for patients. These pamphlets need to be accessible from the Internet, and they need to be housed on a Capital Health Web page. Main components – The main components were cataloguing the content, designing a user-friendly Web page, ensuring ongoing cataloguing is sustainable, and educating users. Setting – Capital Health, Halifax, Nova Scotia. Participants – Eleanor King, Patient Education Coordinator, Capital Health; Myrna Lawson, Library Technician; Penny Logan, Manager Library Services; Pearl Murphy, Web Coordinator; Boyd Sharpe, Systems Analyst; Deb Cameron, Graphic Designer. Program – The program was to ensure Web access to patient pamphlets. Results – 679 patient pamphlets were catalogued and presented on a user-friendly Web page in a searchable database that is controlled in-house and that can easily be kept up-to-date with current staff and systems. Conclusion – Library software and expertise can be used for more than just a catalogue of books and journals. By using already-available software and expertise, maintaining the pamphlets database can be accommodated without additional expense.
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.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.019 |
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".