Bibliographic record
Abstract
Self-denigration in pharmacy: Actions that should be curtailed Good actions give strength to ourselves and inspire good action in others.-PlatoIn the last issue of the journal, we highlighted various words that pharmacists adopt or that are used by others to describe us in a denigrating way as professionals.These include the terms "minor" ailments, "nonmedical" prescribing, "retail" pharmacy, "customers" and so on. 1 We have pledged to avoid the use of these terms in this journal going forward.Just as we can carefully choose our words and their lexical semantics, we can also command our actions through our service to our patients and society.Actions help define us as professionals and set up how we are perceived by others.Our individual and collective actions as pharmacists can reflect a positive image or can belittle and demean our profession.Unfortunately, we sometimes choose actions that fall into the latter category.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".