Bibliographic record
Abstract
has recently emphasized the need to increase attention to the quality of health care in that country, including medication use. 1,2 Having reviewed the literature on adverse outcomes of drug therapy, I suspect that the situation in Canada and Western European nations with respect to the safety and effectiveness of health care is similar to that in the United States. To address deficiencies in these areas, cooperative systems are needed both within health care settings (e.g., ambulatory care) and between settings (e.g., ambulatory and hospital care). Such cooperative systems, of which pharmaceutical care and seamless medications management systems are necessary components, offer clear possibilities of greater safety and effectiveness. The road to medications management is not a broad, smooth motorway. Parts of this road are unpaved and unmarked, and the trip has turned out to be longer and rougher than many of us anticipated 10 years ago. Despite the efforts of European and North American practice researchers, practitioners, and pharmaceutical societies over the past decade, we still have some distance to cover. Neither medications management nor pharmaceutical care has become a de facto, let alone de jure, standard for the safe and effective use of medications. Expectations for the quality of medication use remain minimal, compared with what we could accomplish. Because the road is so long, pharmacists should learn to find satisfaction in the journey itself and to take pride in the significant progress that has already ix
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.269 | 0.192 |
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".