Development of a Data-Driven, Non-Inflationary Author-Credit Allocation Model in Multi-Author Publications in Pharmacy Practice.

OBJECTIVE: To derive and validate a non-inflationary author-credit allocation model (schema) from empirical author-credit allocation data provided by primary authors of multi-author pharmacy practice publications. METHODS: Primary authors of pharmacy practice faculty publications were surveyed in 2 studies (derivation and validation). Respondents' demographic information and their allocation of non-zero percentage credits to authors based on each author's contributions to the publication, were collected for documents with 2-5 (derivation), and 2-6 authors (validation). Lack-of-fit (LOF) index and other error metrics were used to assess the fit of existing non-inflationary schemas (fractional-equal, arithmetic, geometric, golden share, and harmonic) with the empirical data. Empirical data patterns were used to derive variants of the schema with the lowest error metrics (best fit). The fit of the derived schema was validated in the validation study. RESULTS: Unique respondents (155 and 99) reported 232 and 152 documents in the derivation and validation studies, respectively. The credit-allocation pattern shows a decrease from the first author to the penultimate author, with the last author receiving the second-highest credit. A novel schema consistent with the harmonic pattern, with a unique "last-author-bump" (harmonic-LAB) was derived, and it shows the best fit in the validation study. CONCLUSION: Author-credit allocation in pharmacy practice is inconsistent with the whole-count method that inflates publication credits and unfairly allocates equal credits to all co-authors. The harmonic-LAB schema is an evidence-based alternative to authors' statement of percentage contributions. It is included in AuthormetriX (https://sadeosun-a-uthormetrix-v1.streamlit.app/) to facilitate adoption and use in evaluating faculty scholarly output.