Bibliographic record
Abstract
Weeneebayko General Hospital in Moose Factory, Ontario, is a 30-bed acute care hospital that serves the 9000 residents living on the James Bay coast of Ontario. Most of the population consists of Native Canadians living in 6 communities, 4 of which are accessible only by plane or by boat. Traditionally, the hospital pharmacy was to be staffed with one full-time hospital pharmacist. There are also 2 community pharmacies in the region, 1 in each of the larger communities of Moosonee and Moose Factory. Over the years, the Weeneebayko General Hospital had trouble recruiting and retaining personnel for the hospital pharmacist position, and in 2004, the hospital again found itself with no pharmacist in this position. Although arrangements have sometimes been made for a consultant pharmacist when the pharmacist position was vacant, care was compromised during these periods of nonavailability. In 2004, in an attempt to find a long-term solution, a telepharmacy model of care was implemented. Kevin McDonald, a pharmacist who was familiar with the hospital and its staff through previous work on site, started serving the hospital from a home office in a different city, supplemented by twice-yearly on-site visits. Telepharmacy can be defined as the use of electronic information and communications technologies to provide and support pharmaceutical care and distribution of medications when distance separates the pharmacist from the hospital. The model was designed with the intention of duplicating as many of the services that a pharmacist would provide if he or she were on site as possible.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".