Bibliographic record
Abstract
J C P H – Vol. 61, n 5 – septembre –octobre 2008 392 embraced within the care team. Like all of my administrative pharmacist peers, I have a duty to ensure that pharmacists working on the front lines, interacting directly with patients, physicians, and nurses, have the support and opportunity to fulfill the dream of being recognized as essential and respected members of the health care team, with particular responsibility for managing medication therapy. Now that the gate is open, it is up to us to work together on all fronts to achieve whatever we can, offering an expanded model of care and a variety of options for the Canadian public. We are well along on this path in hospitals, but there is room to grow and extend full services to all patients, to achieve continuity of care with our colleagues working in the community as the patient transitions through different care settings, and to engage with policy and operational decision-makers to ensure they both understand our value and comprehend how we can assist in meeting the overall demands on the health care system over the next 20 years. With all of the evidence now available in Canada to support the value of pharmacists, it is time for each of us to pick up the baton and run for the gold—the best possible outcome and experience for every patient who is receiving medication therapy.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
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".