Bibliographic record
Abstract
As you provide pharmaceutical care to your patients, you intervene to alter the drug therapy of individual patients to give them the greatest opportunity of achieving the desired therapeutic goal. This represents a great service to your patients, and thousands of Canadians across the country benefit from pharmacists’ involvement in their care. However, many of the problems requiring intervention from pharmacists occur over and over again. For example, you have probably intervened many times to have the dose of a medication altered because of impaired or improved drug clearance, such as occurs with changes in renal function. Similarly, recent interest in medica tion reconciliation reflects a recognition that interventions to address inappropriate drug therapy have been frequently required at points of transfer in care. The challenge to all care providers, including pharmacists, is to recognize situations in which drug therapy is repeatedly suboptimal and to initiate processes so that future patients will not have the same experi ence. With this in mind, are you recognizing the contributory factors to suboptimal care, and are you doing something about these problems? In this issue of the CJHP , Louie and others 1 describe a specific practice environment in Canadian hospitals where, they suggest, pharmacists and other care providers are not adequately organized to systematically document medication errors, evaluate the causes of the errors, and prevent subsequent problems with drug therapy. These investigators selected the intensive care unit setting for examination because of the critical nature of patients’ conditions, the major contribution of drug therapy to patients’ outcomes, and the potential for deleterious consequences with drug misadventures. Yet their findings could be extrapolated to many other practice environments where pharmacists are significant contributors to care. Louie and others were looking for structured methods of reporting, evaluating, and responding to medication errors, but I am sure that their examination could be expanded to investigate all types of drug-related problems. I am not suggesting that every intervention by a pharmacist is in response to a medication error, but I think the authors’ inquiry into why pharmacists’ interventions are not used as a method for tracking sub optimal care has validity. How often have you stopped to ask yourself, “Why do I need to perform this intervention?” rather than just going ahead with the intervention? I suspect that only infrequently do you investi gate the causes of a recurring drug-related problem and that much more frequently you just intervene. By doing so, are we pharmacists not simply allowing the same thing to happen again in the future? Are we not too easily accepting the status quo as the way it has to be? The challenge to us all, whatever our practice environ
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.123 | 0.108 |
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".