ReviewBritish journal of clinical pharmacology2026
Evidence to support integrating feedback best practice for computer-based assessment in pharmacology education.
Review in British journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Formative feedback to improve pharmacology learning: Proposed principles and guidelines for effective practice.British journal of clinical pharmacology · 2026Guideline
- Evidence to support integrating feedback best practice for computer-based assessment in pharmacology education.British journal of clinical pharmacology · 2026Review
- Using assessment in pharmacology to drive learning in the right direction.British journal of clinical pharmacology · 2026Article
- Helpful if "Medication took up more space in the course!" - a mixed methods study about pharmacotherapeutic knowledge and digital quizzes for learning and assessment in the medical programme.BMC medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Feedback is the most powerful driver of learning, but it can afford variable effects depending on the method used. The design of feedback for computer-based assessment-now increasingly prevalent in higher education-remains relatively underexplored, particularly for pharmacology education. The growing momentum of curricular evolution in pharmacology education presents a timely opportunity to appraise and improve feedback practices in computer-based assessment within the discipline. While there is extensive literature which outlines the principles of effective feedback, there is not a universal model we can apply with consistently positive outcomes. This review explores how empirical evidence can inform the development of effective feedback for use in computer-based assessment. Drawing on findings from multiple meta-analyses and primary research exploring elements of feedback design from across disciplines, this review confirms that elaborate feedback is more effective than corrective feedback alone. However, to support students to develop metacognitive skills for life-long learning, feedback should be tailored to the characteristics of the learner with dialogic support necessary to optimize impact. It is also clear that a key prognostic indicator of successful feedback for performance and satisfaction is clear links to goals and standards. This review argues for feedback for computer-based assessment to go beyond that which is efficient. Instead, it should be constructively aligned with clear goals, and be learner-centred and dialogic to ensure we support the development of reflective and capable graduates of pharmacology education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.