The Online Presence of Physical Consumer Health Information Collections in Multisite Toronto Hospitals
Bibliographic record
Abstract
This study investigates the online presence of physical (on-site) consumer health information collections (CHICs) on the public Web sites of three multisite hospitals in the City of Toronto. All three are affiliated with the University of Toronto, Faculty of Medicine. They are located in downtown Toronto, north Toronto, and northwest Toronto, respectively. The Web pages of multisite hospital-based CHICs contain basic information about hours of operation, phone number, e-mail address, and location. Most provide information about collections (print and digital), lending policies, and information services. Although staffing is mentioned in the context of profession, as librarians, patient educators, or volunteers, staff names and qualifications are not mentioned. The CHIC Web pages of two of three multisite hospitals have online public access catalogs (OPACs) to search their collections. As well, both provide multilingual information resources to better serve the linguistic diversity of Toronto residents. Unique features include access to consumer health and medical online databases through the Web page for its consumer health information collections at one multisite hospital, Humber River Hospital, and information about health-related mobile apps, access to e-books, and customized newsletters in PDF file format for individual consumer health information collections at another multisite hospital, University Health Network.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".