EDITORIAL Evidence to Support Development of Pharmacy Services: How Much or How Little
Bibliographic record
Abstract
We are all challenged by limited resources of time, personnel, and funding in our various practice settings. Yet despite these limitations, there are many demands and expectations to develop and expand pharmacy services. These demands come from various sources, including accreditation standards such as the Required Organizational Practices of Accreditation Canada, 1 colleagues within our facilities, our own clinical practice interests, and elsewhere. Ideally, the premise for a new or expanded pharmacy service will be incontrovertible evidence from randomized controlled trials demonstrating benefit in terms of clinical outcomes and sound economic evidence demonstrating that the maximum benefit is being generated from the resources consumed. 2 However, we often do not have the luxury of these high levels of evidence, which raises the following question: When do we have enough evidence to support the development of a new pharmacy service? There are 2 extreme and opposing perspectives about the necessity of robust evidence from clinical trials to help guide decision-making. The first is that we should implement only those programs that have evidence from randomized controlled trials demonstrating significant improvements in clinical outcomes. With this perspective in mind, I am reminded of a systematic review conducted by Smith and Pell. 3 Although these authors wanted to determine if parachutes were effective in preventing major adverse outcomes after “gravitational challenge ” by summarizing the evidence from randomized controlled trials, an extensive literature search yielded no such trials. Even in the absence of this “gold standard ” level of evidence, however, many of us have jumped from an airplane or other structure and have trusted that a parachute would significantly reduce the risk of an adverse outcome. The second and opposing perspective is to adopt good ideas with little or no evidence except that “it just makes sense”. In my pharmacoepidemiology course, I use examples
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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 | 0.019 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".