Bibliographic record
Abstract
Everyone hopes that what they do makes a difference—an impact. In the case of CPJ and our focus on evidence for pharmacist care and tools for practice, how do we measure that impact? First of all, what is impact? The traditional idea of impact measurement focused on elements of research outputs that were easily quantifiable. Numbers of papers published, impact factors of journals and numbers of citations per article can be assessed fairly easily using bibliographic databases and have long been used to inform academics’ tenure and grant-funding decisions. A journal’s impact factor is derived by dividing the number of citations (references) to the journal for the current year by the number of articles published in that journal in the past 2 years.1 As such, it is a general measure of the influence of the journal itself, rather than the individual articles published within. Another limitation of this measure is that only journals listed in InCites Journal Citation Reports have impact factors calculated, but it certainly doesn’t mean that other journals have no impact! In fact, practice journals read by clinicians may result in even more direct impact on patient care, as the articles are used specifically to inform what they do. Therefore, while traditional impact factors may work well for basic science and other research publications, they might not capture the impact of clinical and practice publications as effectively since implementation by clinicians does not leave a visible trail in the form of citations. Even academics and policy makers are now questioning the suitability of the traditional impact measures. If we publish a guideline or practice innovation in CPJ and it is adopted by pharmacists, is that impact? Not according to more traditional measures because, as mentioned previously, it is unlikely that these practitioners would write papers that reference the original work. But have we had an impact? On patient care, absolutely yes. And that’s the objective. So how do we measure the real impact of a paper? As social networks grow in importance for the sharing of research, alternative article-level metrics such as tweets, blog posts and Facebook shares are now being counted as yet another means of monitoring reach and impact. While most often used to track references to articles, they can also be used to track references to association guidelines, conference presentations, practice tools or any other type of work. Each of these measures, however, provides but a partial picture of the overall impact of any research program. Even when used in conjunction with one another, they tend to assess volume of and references to the knowledge outputs, rather than the influence of the research on practice and actual health outcomes.2 True impact is achieved when research is used to improve service quality by changing how health care is delivered either at the individual practitioner level or through informing broader health policy and guidelines.3 This influence is much more difficult (some might say impossible) to track and measure but is more meaningful for determining the true value derived from the investment in research. So what does this all mean? For researchers, you want to publish where your paper will make a difference to patient care. And, we would argue, that is CPJ. Our print circulation has recently quadrupled to more than 17,000, and we are available in more than 7000 libraries around the world. For the researchers’ bosses, who want to see traditional impact factors, we implore you to think about more relevant forms of impact. Impact that makes a difference to patient care. For clinicians, we know that you want a source of trusted, peer-reviewed material to apply to your practice. That is CPJ. Moving forward, Trish (who has just joined CPJ’s editorial board) and I will start monitoring more substantive measures of impact of publications in CPJ (see Box 1). And if you see something in the journal that resonates, please write to us (that’s another measure of impact).■ Box 1 New measures of impact for clinical/practice publications Tweets Facebook posts Page views Downloads Blog posts Newsletter references Letters to the editor Google Analytics
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 | 0.015 | 0.196 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.036 | 0.067 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.043 | 0.030 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.316 | 0.167 |
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".